The entity evolutionary coupling analysis of software systems is helpfulfor analysis activities such as co-change candidate prediction, risk identification of software supply chain, code vulnerability traceability, defect prediction and architecture problem localization. The evolutionary coupling between two entities indicates that these entities tend to be changed together in the software revision history. Existing methods present a low accuracy to detect the frequent "having distance" co-change in the revision history. To address this problem, this study proposes an evolutionary coupling analysis method based on the combination of association rule mining, episode mining and latent semantic indexing (association rule, MINEPI and LSI based method, AR-MIM), which mines co-change relations of "having distance". The experiment verified the effectiveness of AR-MIM by compared with the four baseline methods on the dataset, collecting 58 Python projects, 242 074 pieces of training data, and 330 660 pieces of ground truth. The results show that the precision, recall, and F1 score of AR-MIM are better than those of existing methods in co-change candidate prediction.