In the research on causal discovery of time series data, the traditional algorithm analyse the causal relationship between time series data in the time window, which has problems such as limited causality recognition accuracy and high algorithm complexity. In order to solve this problem, this paper first defines the summary causal diagram, causal summary mutual information and conditional causal summary mutual information, derives the temporal series variable orientation rule based on causal mutual information, and then distinguishes whether there are confounding factors, and proposes improved Peter and Clark summary mutual information(PCSMI) and fast causal inference summary mutual information(FCISMI) algorithms combined with Peter and Clark(PC) and fast causal inference(FCI) algorithms, respectively. Experimental results show that the improved algorithm can effectively improve the accuracy of causal discovery of time series data under low complexity conditions.