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In the arrhythmia automatic recognition system,it is difficult to extract signal features because of the complex ECG morphology. The Automatic classification model has low accuracy and adaptability. Aiming at the above problems, a recognition and classification method of ECG signal semantic segmentation based on u-net neural network is designed.With the operation rules of the full convolution neural network, the location and category of beats in the signal segments can be classified by taking ECG signal fragment data as input and label map as output.Simulation results show that the proposed method has achieved high accuracy in five classification problems of normal sinus beat, left bundle branch block, right bundle branch block, atrial premature beats and ventricular premature beats, and achieved effective recognition of arrhythmia signals.