Aiming at the problem of low clinical practicability and accuracy in the clinical diagnosis of myocardial infarction， an auxiliary diagnosis method of myocardial infarction based on 12-lead ElectroCardioGram （ECG） signal was proposed. Firstly， denoising and data enhancement were performed on the 12-lead ECG signals. Secondly， aiming at the ECG signals of each lead， the statistical features including standard deviation， kurtosis coefficient and skewness coefficient were extracted respectively to reflect the morphological characteristics of ECG signals， meanwhile the entropy features including Shannon entropy， sample entropy， fuzzy entropy， approximate entropy and permutation entropy were extracted to characterize the time and frequency spectrum complexity， the new mode generation probability， the regularity and the unpredictability of the ECG signal time series as well as detect the small changes of ECG signals. Thirdly， the statistical features and entropy features of ECG signals were fused. Finally， based on the random forest algorithm， the performance of algorithm was analyzed and verified in both intra-patient and inter-patient modes， and the cross-validation technology was used to avoid over-fitting. Experimental results show that， the accuracy and F1 value of the proposed method in the intra-patient modes are 99.98% and 99.99% respectively， the accuracy and F1 value of the proposed method in the inter-patient mode are 94.56% and 97.05% respectively； and compared with the detection method based on single-lead ECG， the detection of myocardial infarction with 12-lead ECG is more logical for doctors’ clinical diagnosis.