Abstract: The incidence rate and mortality of cardiovascular diseases in China are increasing year by year. However, due to the large scale and complexity of electrocardiogram data, clinical medical staff have a heavy workload and are prone to misdiagnosis or missed diagnosis during electrocardiogram screening. Based on this, in this paper we proposes an intelligent classification and analysis of multi-lead electrocardiogram signals based on multi feature branch convolutional neural networks using CPSC-2018 twelve lead data. Firstly, divide the CPSC-2018 12-lead data into 9 categories, derive 8-lead electrocardiogram signals based on the 12 leads, and extract local features separately. Then, the attention weight vectors of different categories are calculated through bidirectional GRU encoding and attention mechanism, and the feature information is concatenated and fused into feature vectors to achieve multi-lead electrocardiogram classification. The experimental results showed that good classification performance was achieved on the validation set, with an F1 value of 81. 2% for normal categories and an average F1 value of 84. 2%. Especially, when identifying two types of arrhythmia, atrial fibrillation（AF） and right bundle branch block（RBBB）, F1 values reached 95. 1% and 93. 1%, respectively.