模式识别与人工智能是推动当今科技进步与产业升级的核心领域之一。人工智能技术的快速发展，结合深度学习和大数据处理的能力，极大提升了模式识别在图像、语音识别、自然语言处理和自动驾驶等前沿领域的应用。同时，这些技术正在医疗健康、智能交通和金融风控等行业广泛落地，为社会带来深远影响。此外，生成式AI、多模态学习、模型解释性及边缘计算等创新方向进一步拓展了这一领域的技术潜力和应用场景。

2026年第九届模式识别与人工智能国际会议将于2026年8月14-16日在中国·上海举办。本次大会由上海交通大学主办，上海交通大学人工智能学院承办，旨在搭建全球专家、学者与从业者的学术交流与合作平台。会议主要围绕模式识别与人工智能领域相关主题，热忱邀请该领域专家、研究人员和专业技术人员踊跃投稿并参加大会。为保证本次会议的学术质量，吸引更多的原创高水平学术论文，现公开征稿，欢迎广大从事模式识别和人工智能研究的科研人员、教师及学生踊跃投稿。

The 9th International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026) will be held from August 14-16, 2026, in Shanghai, China. Sponsored by Shanghai Jiao Tong University, IEEE; hosted by the College of Big Data and Information Engineering of Shanghai Jiao Tong University, the conference aims to establish a platform for academic exchange and collaboration among global experts, scholars, and professionals. The conference mainly focuses on themes related to pattern recognition and artificial intelligence. We warmly invite experts, researchers and professional technicians in this field to submit papers and attend the conference. In order to ensure the academic quality of this conference and attract more original high-level academic papers, we now openly call for papers. We welcome scientific researchers, teachers and students engaged in pattern recognition and artificial intelligence research to submit papers.

Pattern recognition and artificial intelligence are among the core fields driving technological advancement and industrial transformation today. The rapid development of artificial intelligence, combined with the capabilities of deep learning and big data processing, has significantly enhanced the application of pattern recognition in cutting-edge areas such as image processing, speech recognition, natural language processing, and autonomous driving. These technologies have also been widely adopted in industries like healthcare, intelligent transportation, and financial risk management, bringing profound societal impacts. Furthermore, innovative directions such as generative AI, multimodal learning, model interpretability, and edge computing have further expanded the technological potential and application scenarios of this field.