Weakly supervised video anomaly detection has become a hot spot in video anomaly detection research due to its strong anti-interference and low data labeling requirements. In the existing methods， most of the weakly supervised video anomaly detection methods assume that the clips in each video distribute independently， and determine whether it is abnormal for each video clip independently， ignoring the temporal and spatial information between video clips. To alleviate these problems， this paper proposes a weakly supervised anomaly detection method based on spatio-temporal dependence and feature fusion. Retaining the original characteristics of video clips， this method uses the distance of index and the similarity of features between video clips to fit the time dependence and the spatial dependencies of video， which builds the relationship characteristics of video clips. By fusing the original features and relationship features， the dynamic characteristics and temporal relationship of videos can be better expressed. Extensive experiments on two benchmark datasets， UCF-Crime and ShanghaiTech， demonstrate that the proposed method outperforms other methods with the AUC values reaching 80.1% and 94.6%， respectively.