Abstract: The analysis of electrocardiogram (ECG) signals is of great significance in predicting arrhythmias and preventing cardiovascular diseases. However, ECG data has high noise and highly temporally correlated temporal characteristics, which challenges machine learning algorithms in classification tasks. This paper proposes an ECG signal classification method based on the bidirectional long short-term memory (BiLSTM) network and multi-task temporal attention, named BiLMTA, which combines a time series encoding module and a multi-task decoding module. Specifically, the first step of the method involves feeding the filtered ECG signals into the encoder module, in which BiLSTM is utilized to model temporal dependencies between preceding and succeeding time steps. Subsequently, the multi-task decoding module handles two key tasks: one is responsible for the main task, the classification task of ECG signals; the other serves as an auxiliary task to predict normal and abnormal heartbeats. To better capture the key information in ECG signals and improve sensitivity to abnormal patterns, a temporal attention mechanism is introduced. In addition, in order to promote the interactive learning between two tasks, a new joint loss function is proposed. This loss function comprehensively considers the cross entropy loss of the main task and auxiliary task to ensure that the two tasks can jointly optimize the model and improve the accuracy of classification results. The experimental results show that the BiLMTA method performs well on two publicly available datasets, surpassing traditional methods and state-of-the-art deep learning methods.