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Due to the influence of scene, visual angle, illumination, scale change and local deformation, the recogni- tion accuracy of overlapping target, crowded target and small target is low. An improved multibranch Resnet50 convolutional neural network is proposed to improve the accuracy of multi-objective recognition. First, while retaining constant maps after the first convolutional residual block layer1; Secondly, add as many 1 × 1 short branch as retaining original features; embed a Space_Channel Attention Mechanism Module (CBAM) that modify the activation function RELU6 in parallel; Lastly, the last three feature graphs are fused. The fused feature layer focuses on the more significant information in space and channels, thus enhancing the feature expression ability of feature graphs, so that Convolutional Neural Network (CNN) can obtain more discriminant features, thus greatly improving the accuracy of object recognition. Comparative experiments which are carried out on fashionmnist and cifar10 data sets shows that the improved resnet50 algorithm is a target recognition model with a compromise between accuracy and speed .