Aiming at the limitation of using attention mechanism only on the time -frequency dimension and the insufficient feature extraction caused by the single convoluti onal layer in sound event detection,a convolutional recurrent neural network (CRNN) model wit h multi-scale attention feature fusion is proposed to improve the sound event det ection performance.Firstly,a multi-scale attention module is proposed,which pays mu lti-scale attention to the local time-frequency units and global channel features and imp roves the feature selection ability of the network.Secondly,a multi-scale feature fusio n method is proposed.The method is used to fuse multi-scale attention feature with rich co ntext information and improve the feature expression ability of the network.Finally, the bidirectional gated recurrent units layer is used to model the time dependence a nd the fully connected layer is used to achieve sound events sort frame by frame.In addition ,data balancing technique is used to further generalize the model.The experimental re sults on a sub-dataset of Audio Set show that,compared with CRNN,the proposed network mo del makes error rate (ER) reduce by 11% and F1-score (F1) increase by 8.3% of the evaluation dataset,which effectively improves the performance of sound event detection.