The detection and classification of moving targets have always been a key technology in intelligent video surveillance. Current detection and classification algorithms for moving targets still face many difficulties, mainly because of the complexity of the monitoring environment and the limitations of target characteristics. Therefore, this article conducts corresponding research on moving target detection and classification in intelligent video surveillance. According to the Gaussian Mixture Background Model and Frame Difference Method, this paper proposes a moving target detection method based on GMM (Gaussians Mixture Model) and Frame Difference Method. This method first proposes a new image combination algorithm that combines GMM and frame difference method, which solves the problems of noise and voids inside the target caused by the fusion of traditional GMM and frame difference method. The moving target detection method can effectively solve the problems of incomplete moving target detection, target internal gap, and noise, and it plays a vital role in the subsequent moving target classification process. Then, the method adds image inpainting technology to compensate the moving target in space and obtain a better target shape. The innovation of this paper is that in order to solve the multiobject classification problem, a binary tree decision support vector machine based on statistical learning is constructed as a classifier for moving object classification. Improve the learning efficiency of the classifier, solve the competitive classification problem of the traditional SVM, and increase the efficiency of the mobile computing intelligent monitoring method by more than 70%.

With the continuous development of big data and the continuous improvement of people’s living standards, increasingly attention is paid to physical health. Swimming in this sport is effective in preventing the occurrence of arthritis. This paper analyzes the prevention and exploration of arthritis and relies on the traditional method of retrieving clinical literature on the treatment of knee osteoarthritis with traditional Chinese medicine and internal medicine, which requires a lot of manpower and material resources. At this time, the role of data mining technology is brought into play. This article analyzes the prevention of arthritis by swimming. If you rely on the traditional retrieval of clinical literature on the treatment of knee osteoarthritis with traditional Chinese medicine and internal medicine, you will find a lot of disordered data. It takes a lot of manpower and material resources to sort out the summary, and at this time, the role of data mining (DM) technology is brought into play. In this paper, the relevant information of the literature that meets the requirements is established in an Excel database, and the data of the relevant information is entered. Through sorting and analysis, the TCM syndrome types of knee osteoarthritis are summarized. Then, DM technology was used to carry out statistical analysis of frequency and prescription, to summarize the distribution characteristics of the corresponding knee osteoarthritis, TCM syndrome types, and the weight of each syndrome type, and to make a preliminary discussion at the same time. Finally, it is concluded that there are better prevention methods for arthritis in the research methods of traditional Chinese medicine. DM technology has been increasingly applied to all aspects of traditional Chinese medicine. DM technology has improved its research efficiency by 38% and achieved great results, which will play a greater role in promoting the research process of TCM syndrome.