2026 7th International Conference on Computers and Artificial Intelligence Technology (CAIT 2026)
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About this event
2026 7th International Conference on Computers and Artificial Intelligence Technology (CAIT 2026)
Event Dates: November 13–15, 2026
Venue: Guiyang, Guizhou, China
Organized by: College of Computer Science and Technology, Institute of Artificial Intelligence, Guizhou University
Patrons: Macau University of Science and Technology, Beijing University of Posts and Telecommunications, Guangxi University, Inner Mongolia University of Technology, SIIT, Shenzhen Smart City, Lanzhou Institute of Technology, Hangzhou Polytechnic University
About CAIT 2026: The 2026 IEEE 7th International Conference on Computers and Artificial Intelligence Technology (CAIT 2026) is a premier event dedicated to advancing research and innovation in the rapidly evolving fields of computer science and artificial intelligence. As technology continues to integrate computing and AI, this conference serves as a global platform for leading researchers, practitioners, and industry experts to convene, share knowledge, exchange ideas, and explore the latest trends and developments.
Key Focus Areas:
- Advanced algorithms and machine learning techniques
- Natural language processing and its applications
- Automation, prediction, and optimization across industries
- Digital economy-oriented artificial intelligence
- AI for next-generation intelligent services
- AI-based optimization and management in cloud computing
Target Audience:
- Academic researchers in computer science and AI
- Industry professionals and technology developers
- Policy makers and technology strategists
- Students and educators in related fields
- Representatives from finance, healthcare, manufacturing, and transportation sectors
Conference Proceedings: All submitted papers will undergo a rigorous peer-review process. Accepted papers will be published in the Conference Proceedings and available online and in IEEE Xplore. Previous editions of CAIT have been indexed in EI Compendex, SCOPUS, and other reputable academic databases.
SCI-Journal Publication: High-quality papers may be selected for extension and submission to the CAAI Transactions on Intelligence Technology, a leading open-access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI), with a CiteScore of 14.5 and an Impact Factor of 5.1.
Special Sessions:
- Digital Economy-Oriented Artificial Intelligence
Chair: Di Han, Guangdong University of Finance - AI for Next Generation Intelligent Services
Chairs: Wei William Wang, Macao Polytechnic University; Thippa Reddy Gadekallu, Zhejiang A&F University; Ali Kashif Bashir, Manchester Metropolitan University - AI-based Optimization and Management in Cloud
Contact: cait_conference@163.com
Data sheet
2026 7th International Conference on Computers and Artificial Intelligence Technology (CAIT 2026) – Event Attendee & Buyer Profile Analysis
Event Date: November 13–15, 2026
Location: Guiyang, Guizhou, China
Event Status: Upcoming
Research Date: June 29, 2026
Event Overview
| Event Name | 2026 7th International Conference on Computers and Artificial Intelligence Technology (CAIT 2026) |
| Event Date | November 13–15, 2026 |
| Event Status | Upcoming |
| Venue | Not publicly specified on the official event text provided; event location listed as Guiyang, Guizhou, China |
| City | Guiyang |
| State / Region | Guizhou |
| Country | China |
| Organizer | College of Computer Science and Technology, Institute of Artificial Intelligence, Guizhou University |
| Official Event Website | cait.net |
| Event Type | Academic conference / research symposium / technical paper conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training; Business Services |
| Audience Reach | Global academic and applied-AI audience, with strongest reach in China and wider Asia-Pacific research networks |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low to medium; official site confirms the event format and dates, but no public attendance figure was provided in the supplied source text. |
| Main Purpose of Event | To bring together researchers, practitioners, and industry experts to present peer-reviewed work, share advances in AI and computing, and build academic and industry collaboration. |
About the Event
CAIT 2026 is the 7th edition of an IEEE-branded international conference focused on computers and artificial intelligence technology. The official program emphasizes advanced algorithms, machine learning, natural language processing, automation, prediction, and optimization, with conference proceedings intended for rigorous peer-reviewed publication and post-conference research dissemination.
The event matters because it sits at the intersection of academic research, applied AI development, and industry adoption. Its participant mix is likely to include university researchers, AI faculty, doctoral candidates, technical practitioners, and innovation-focused organizations seeking collaboration, publication opportunities, and exposure to emerging methods that can be translated into finance, healthcare, manufacturing, transportation, cloud computing, and digital-economy applications.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University researchers and faculty | Universities, colleges, AI labs, research institutes | Influence on technology selection, lab procurement, collaboration, publication, and grant-funded projects | High relevance for AI platforms, cloud compute, datasets, software tools, and research partnerships |
| Graduate students and postdoctoral researchers | Universities and research centers | Influence on tool adoption, research software usage, and future enterprise buying committees | Useful for awareness, technical evangelism, and future pipeline building |
| AI and data science leaders | Technology firms, innovation teams, digital transformation units | Technical decision-making and solution evaluation | Strong fit for AI software, model deployment, MLOps, cloud, and analytics vendors |
| R&D and innovation managers | Enterprise innovation labs, industrial R&D groups, public-sector labs | Budget influence, pilots, experimentation, and vendor shortlisting | Relevant for emerging AI, automation, and research-grade software offerings |
| IT and platform engineering leaders | Cloud teams, platform engineering, enterprise IT | Implementation and infrastructure decision influence | High relevance for compute, storage, security, data pipelines, and integration tools |
| Government and public-sector digital teams | Smart city programs, public research bodies, education authorities | Procurement and policy influence for AI adoption | Relevant where AI is being applied to public services, city systems, and digital governance |
| Industry practitioners and technical consultants | Consulting firms, systems integrators, solution partners | Recommendation, specification, and implementation influence | Useful for channel development and referral relationships |
| Journal editors, reviewers, and association leaders | Academic associations, publication networks, IEEE-affiliated communities | Influence over research visibility, community partnerships, and event credibility | Important for sponsorship, institutional partnerships, and thought-leadership outreach |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Guiyang / Guizhou host market | Local universities, public institutions, tech teams, students | Medium | Strong local academic participation expected because the host organization is Guizhou University |
| Southwest China | Regional universities, labs, and digital economy stakeholders | High | Likely to attract attendees from nearby provinces and academic corridors |
| Major Chinese academic hubs | Beijing, Shanghai, Guangdong, Zhejiang, Sichuan, Hubei | High | Patron institutions and conference topic relevance support cross-province participation |
| Greater China / APAC | Hong Kong, Macau, Taiwan, Singapore, Malaysia, Japan, South Korea | Medium | International academic reach is plausible for an IEEE conference, though attendance is not publicly confirmed |
| Global research community | Researchers, reviewers, journal-linked contributors | Medium | Conference proceedings and publication pathways can attract international paper submissions |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The conference is positioned as an international IEEE-branded research event with cross-border academic and industry relevance. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Guizhou University | Host university / research institution | Official host and sponsor; core stakeholder for research, lab, and academic collaboration | gzu.edu.cn | Dean, Professor, Lab Director, Research Director, IT Director | Confirmed Government / Procurement Organization |
| College of Computer Science and Technology, Guizhou University | Academic department | Host organization; likely central to session leadership, submissions, and technical collaboration | gzu.edu.cn | Department Chair, Faculty Lead, Research Group Leader | Confirmed Government / Procurement Organization |
| Institute of Artificial Intelligence, Guizhou University | Research institute | Host organization focused on AI research and implementation | gzu.edu.cn | Institute Director, Principal Investigator, Research Scientist | Confirmed Government / Procurement Organization |
| Macau University of Science and Technology | Patron university | Named patron; indicates regional academic collaboration and likely participation | must.edu.mo | Professor, Research Director, School Dean, PhD Supervisor | Confirmed Speaker Organization |
| Beijing University of Posts and Telecommunications | Patron university / telecom-tech research institution | Strong relevance to AI, communications, computing, and digital infrastructure topics | bupt.edu.cn | Professor, Lab Head, Department Chair, CIO | Confirmed Speaker Organization |
| Guangxi University | Patron university | Named patron; likely contributor to academic attendance and paper submissions | gxu.edu.cn | Dean, Professor, Research Coordinator | Confirmed Speaker Organization |
| Inner Mongolia University of Technology | Patron university | Named patron with engineering and computing relevance | imut.edu.cn | Professor, Research Lead, Faculty Member | Confirmed Speaker Organization |
| SIIT | Patron institution | Named patron in official event text; likely research and education stakeholder | siit.ac.th | Program Director, Professor, Researcher | Confirmed Speaker Organization |
| Shenzhen Smart City | Patron organization / smart-city stakeholder | Highly relevant applied AI and urban technology stakeholder | shenzhen.gov.cn | Digital Transformation Lead, Smart City Program Manager, CIO | Confirmed Government / Procurement Organization |
| Lanzhou Institute of Technology | Patron institute | Named patron; relevant for engineering, computing, and AI research | lut.edu.cn | Institute Director, Professor, Lab Lead | Confirmed Speaker Organization |
| Hangzhou Polytechnic University | Patron university | Named patron; likely to contribute faculty, students, and applied research participation | hzpt.edu.cn | Dean, Faculty Lead, Research Director | Confirmed Speaker Organization |
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Professor / Principal Investigator | Academic / Research | Senior | Drives research direction, collaboration, and lab procurement. |
| 2 | Research Director / Institute Director | R&D | Senior | Owns budgets, partnerships, and technical strategy. |
| 3 | Dean / Department Chair | Academic Leadership | Executive | Influences institutional participation and partnership decisions. |
| 4 | AI Lab Director / Machine Learning Lead | AI / Data Science | Senior | Evaluates AI platforms, tooling, datasets, and compute resources. |
| 5 | IT Director / CIO | Information Technology | Executive | Relevant for platform, cloud, security, and deployment decisions. |
| 6 | Data Science Manager | Analytics / AI | Mid to Senior | Core evaluation role for analytics and ML solutions. |
| 7 | Program Manager / Project Lead | Innovation / Operations | Mid to Senior | Coordinates pilots, deployments, and interdepartmental collaboration. |
| 8 | Systems Architect / Platform Architect | Engineering / IT | Senior | Influences technical stack choices and integration design. |
| 9 | Solution Architect / Technical Consultant | Consulting / Delivery | Mid to Senior | Can recommend vendors and shape implementation paths. |
| 10 | Graduate Research Assistant / PhD Candidate | Academic | Entry to Mid | Early influencer for tool adoption and future procurement preferences. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Broad fit for AI, software, data, and digital transformation buyers | Cloud, AI platforms, integration, cybersecurity |
| 2 | Computer Software | Directly aligned with AI software and research tooling | ML tools, data platforms, SaaS, developer tooling |
| 3 | Higher Education | Core audience of academic researchers and faculty | Research software, lab technology, academic partnerships |
| 4 | Research | Conference is fundamentally research-focused | Research collaborations, grants, technical services |
| 5 | Education Management | Relevant for institutions sending delegates and funding labs | Curriculum tech, e-learning, campus AI initiatives |
| 6 | Government Administration | Public-sector AI adoption and smart-city applications are relevant | Digital government, smart city, public-sector analytics |
| 7 | Internet | AI platform and data-heavy digital businesses attend AI conferences | AI services, recommendation engines, automation |
| 8 | Telecommunications | Relevant to AI, networking, edge, and intelligent services | Network AI, OSS/BSS, smart services |
| 9 | Financial Services | AI use cases in risk, fraud, forecasting, and automation | AI analytics, compliance automation, fraud detection |
| 10 | Hospital & Health Care | The official intro cites healthcare as a key AI application area | Clinical AI, imaging, workflow automation, decision support |
| 11 | Manufacturing | Official text references manufacturing as an application area | Industrial AI, predictive maintenance, quality systems |
| 12 | Transportation/Trucking/Railroad | Transportation is explicitly mentioned in the official overview | Routing AI, fleet optimization, predictive logistics |
| 13 | Logistics & Supply Chain | Optimization and prediction use cases are central to AI adoption | Demand forecasting, planning, automation |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Not publicly confirmed | Unknown | Official event text supplied | Organizer has not publicly posted a verified attendance figure in the supplied source text. |
| Exhibitor count | Not applicable / not publicly listed | Unknown | Official event text supplied | This is a conference, not a trade exhibition with a public exhibitor directory in the supplied text. |
| Buyer count | Not publicly confirmed | Unknown | Official event text supplied | Likely mix of researchers, faculty, and technical practitioners rather than formal buyers. |
| Speaker count | Not publicly confirmed | Unknown | Official event text supplied | Keynote and invited speakers are referenced in the site navigation, but no count was provided. |
| Historical attendance | Not publicly confirmed in supplied text | Historical / prior-year evidence only | Official history navigation references prior editions | Prior-year edition pages may provide additional benchmarking, but no numeric total was included in the provided source text. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Machine learning | Model development, training, evaluation | Research collaboration and technical demos | ML platforms, notebooks, compute, MLOps |
| Natural language processing | Text analytics, multilingual systems | Use-case discussions with researchers and product teams | NLP APIs, LLM tooling, annotation tools |
| Automation and optimization | Process improvement and decision support | Lead generation for applied AI and analytics | Decision intelligence, workflow automation |
| Cloud computing | Scalable training and deployment | Infrastructure and platform conversations | Cloud services, GPU instances, containers |
| Digital economy AI | Commercialization and scalable deployment | Partnerships with enterprise and public-sector teams | AI SaaS, analytics, workflow systems |
| Research publication | Peer-reviewed dissemination and visibility | Thought leadership and institutional branding | Publishing, citation tools, editorial services |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | Strong concentration of academic, research, and applied-AI decision-makers. |
| Decision-maker availability | Medium | Senior faculty and lab leads are relevant, but many attendees will be technical contributors rather than budget owners. |
| Data collection potential | Medium | Likely strong author/speaker metadata, but attendee lists are often limited for academic conferences. |
| Apollo targeting potential | High | Good match to universities, research, IT, software, telecom, and public-sector digital teams. |
| Geographic targeting potential | High | Can target China, APAC, and global research institutions by geography and institution type. |
| Best outreach approach | High-touch / education-led | Use technical messaging, publication value, research outcomes, and partnership benefits. |
| Overall lead quality | High | Best suited for research software, cloud, AI tooling, and academic partnership development. |
| Best use case | B2B attendee list building and research-sector prospecting | Well suited for targeted outreach to institutions and R&D teams rather than broad consumer marketing. |
| Limitations / risks | Moderate | Limited public attendee visibility and fewer pure commercial buyers than a trade show. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services, Computer Software, Higher Education, Research, Government Administration, Internet, Telecommunications, Financial Services, Hospital & Health Care, Manufacturing | Align to the strongest likely attendee and buyer clusters. |
| Departments | Research, Engineering, IT, Data/Analytics, Innovation, Academic Affairs, Product | Capture technical and institutional decision-makers. |
| Seniority | Manager, Director, VP, CXO, Professor, Head, Lead | Prioritize budget holders, technical approvers, and research leaders. |
| Job titles | Professor, Principal Investigator, Research Director, Lab Director, AI Lead, Data Science Manager, CIO, IT Director, Innovation Director, Program Manager | Reach the most relevant contacts for AI, computing, and research tools. |
| Geography | China, Hong Kong, Macau, Singapore, Japan, South Korea, United States, United Kingdom, Germany | Support local, regional, and global research outreach. |
| Employee size | 11–50, 51–200, 201–500, 501–1,000, 1,001–5,000, 5,000+ | Cover universities, labs, public institutions, and enterprise buyers. |
| Keywords | artificial intelligence, machine learning, NLP, computer science, deep learning, optimization, cloud computing, research, AI lab, digital transformation | Refine for event topic relevance. |
| Company type | Education, government, research institute, enterprise technology, public-sector innovation unit | Prioritize organizations most likely to engage with the conference topics. |
| Revenue / funding | Not essential; use where relevant for commercial vendors and startups | Useful for filtering startup and enterprise innovation targets. |
Suggested Apollo Search Logic: (artificial intelligence OR machine learning OR NLP OR computer science OR deep learning OR cloud computing) AND (Professor OR Principal Investigator OR Research Director OR AI Lead OR Data Science Manager OR CIO OR IT Director OR Innovation Director) AND geography: China OR APAC OR Global academic hubs.
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| CAIT Official Website | Official organizer website | Event name, dates, city, country, organizer, sponsor/host/patron organizations, topics, publication pathway, and key dates | High |
| CAIT official site navigation/history pages | Official prior-year archive reference | Evidence of prior editions and continuity of the conference series | High |
| Supplied official event text | Primary source excerpt | Confirmed AI application themes, special sessions, and the list of patrons and host organizations | High |
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