第八届人本计算与数据智能国际会议（HCC 2026）

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We warmly invite researchers, academics, and industry professionals worldwide to contribute their latest original research, case studies, and practical insights.

Topics of interest for submission include, but are not limited to, the following:

Track 1: Human-Centered Data Intelligence and AI

Artificial Intelligence and Machine Learning
Human-Centered AI and Intelligent Systems
Explainable and Trustworthy AI
Big Data Analytics and Data Mining
Knowledge Discovery and Knowledge Graphs
Natural Language Processing and Large Language Models
Data Visualization and Visual Analytics
Multimodal Intelligence and Data Fusion
Intelligent Recommendation Systems

Track 2: Human-Centered Computing Systems and Applications

Human-Computer Interaction and User Experience
Internet of Things and Edge Computing
Cloud Computing and Intelligent Services
Mobile and Ubiquitous Computing
Pervasive Computing and Context-Aware Systems
Social Computing and Collective Intelligence
Human-Robot Interaction and Intelligent Agents
Virtual, Augmented, and Extended Reality
Cyber-Physical Systems and Smart Environments

Track 3: Human-Machine Collaboration and Social Intelligence

Swarm Intelligence and Collaborative Computing
Social Computing and Social Network Analysis
Computer-Supported Cooperative Work
Crowdsourcing and Human-Centered Computing
Virtual Collaboration and Augmented Interaction

Track 4: User Behavior Big Data Modeling and Collective Intelligence Analysis

User Group Feature Mining and Profile Construction Based on Multi-source Behavioral Data
Behavioral Data Fusion and Cooperative Intelligent Decision-Making Methods under Crowdsensing
Mining and Intelligent Prediction of Evolutionary Laws of Collective Behaviors
Heterogeneity Modeling of Collective Behaviors with Multi-scenario Fusion
Intelligent Perception and Judgment of Collective Behaviors in Public Opinion
Associated Collective Behavior Mining and Collaborative Clustering Analysis

Track 5: Explainable Data Intelligence and Human-Centered Trustworthy AI

Research on Visual Explainable Methods for Big Data Analysis Results
Bias Detection and Fairness Optimization of Intelligent Data Algorithms
Research on Temporal Explainable Intelligent Models for Human-Centered Services
Research on Transparency Methods of AI Decision-Making Based on User Trust
Multi-dimensional Data-Driven Algorithm Fairness Evaluation and Correction
Lightweight Explainable Analysis System for Personalized Human-Centered Data

Track 6: Multimodal Human-Computer Interaction Data Intelligence

Intelligent Perception and Analysis of User Emotion via Multimodal Interaction Data Fusion
Lightweight Multimodal Fusion Processing Methods for Natural Human-Computer Interaction
Intelligent Evaluation Model of User Experience Based on Physiological and Behavioral Data