To solve the problem of insufficient spatial and temporal feature in the process of dangerous behavior recognition， this paper improves the traditional dual-stream convolution model and proposes a new dual-steam convolution dangerous behavior recognition model based on CNN-LSTM. In this model， CNN network and LSTM network are connected in parallel. CNN network is used as the spatial flow. The spatial motion attitude information of human skeleton is divided into static and dynamic. These features are fused as the output of the spatial flow. In order to increase the ability of extracting temporal features of human skeleton， an improved temporal sliding LSTM network is used in the time stream. Finally， the two branches are fused in time and space， and the dangerous actions are classified and identified by Softmax. Experimental results on NTU RGB D and Kinetics datasets show that the average cross view（CV） accuracy of the improved model is 92.5% and the average cross subject（CS） accuracy is 87.9%. The proposed method is superior to that before improvement and other methods. It can effectively recognize dangerous human actions and has good discrimination effect for fuzzy actions.