Aiming at the problem that the existing Legal Case Retrieval （LCR） methods lack effective utilization of case elements and are easily misled by similarity of semantic structure of the case content， an LCR method integrating temporal behavior chain and event type was proposed. Firstly， the sequence labeling method was adopted to identify legal event type in the case description， and the temporal behavior chain was constructed by using behavioral elements in the case text， thereby highlighting key elements of the case， so that the model focused on core content of the case， so as to solve the problem that the existing methods are easily misled by similarity of semantic structure of the case content. Secondly， similarity vector representation matrix of the temporal behavior chain was constructed by segmented coding to enhance semantic interaction of behavioral elements among cases. Finally， through the aggregation scorer， relevance of the cases was measured from three perspectives： temporal behavior chain， legal event type， and crime type， so as to increase rationality of the case matching score. Experimental results show that on LeCaRD （Legal Case Retrieval Dataset）， compared with SAILER （Structure-Aware pre-traIned language model for LEgal case Retrieval） method， the proposed method has the P@5 value improved by 4 percentage points， the P@10 value increased by 3 percentage points， the MAP value improved by 4 percentage points， and the NDCG@30 value increased by 0.8 percentage points. It can be seen that this method utilizes case elements effectively to avoid interference of similarity of semantic structure of the case content， and can provide a reliable basis for LCR.