视频数据中包含丰富的运动事件信息，从中检测复杂事件，分析其中的高层语义信息，已成为视频研究领域的热点之一。视频复杂事件检测，主要是对事件中多语义概念进行检测分析，对多运动目标的特征进行描述，发现底层特征与高层语义概念间的关系，旨在从各类视频特征及相关的原始视频数据中自动提取视频复杂事件中语义概念模式，实现“跨越语义鸿沟”的目标。在超图理论的基础上，提出了针对运动目标特征分别构建轨迹超图和多标签超图，并对其进行配对融合，用于检测视频复杂事件。实验结果表明，同其他方法如基于普通图的事件检测方法和基于超图的多标签半监督学习方法相比，该方法在检测复杂事件结果中具有更高的平均查准率和平均查全率。

With the rapid development of the applications of network, enormous multimedia files are emerging every day. Video data is an integration of text, sound, image and other files. Not only being hierarchical, structural and complex, video data is also rich in semantic information. Therefore, extensive attentions have been drawn to how to process video data quickly, extract the video characteristics accurately, and analyze and understand the semantic content deeply. Semantic event detection and analysis could find video information quickly and accurately for users in the vast ocean of video data. It can be applied to the field of video on demand, intelligent monitoring and video mining as well. However, there are still many limitations, such as low recognition rate for multiple moving objects with different characteristics, low accuracy in semantic event detection, difficulties in detecting semantic event correlations, the lack of consistent standards of event semantic description, and so on. The detection and analysis methods of complex event based on matching integration between trajectory and multi-label hypergraphs are proposed. Trajectory and multi-label hypergraphs are constructed for classifying and recognizing the complex events. By matching the trajectory hypergraph and multi-label hypergraph, mapping relationship between trajectory and multiple semantic labels is built to extract the complex semantic events. The recognition of low-level features to high-level semantic is made possible for video events. Compared with other methods, such as event detection method based on graph and multi-label semi-supervised learning methods based on hypergraph, the proposed method has a higher mean average recall rate and mean average accuracy rate in the detection result of the complex event. we propose a new event detection method that is trajectory and multi-label hypergraphs model in this paper. This model is a widely-used detection and analysis of complex semantic-based events method. In their clustering process, their number of vertices and clusters has been more than other graph methods. But, the event detection result is better than others.