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In view of the present shock hammer parts recognition precision rate is low, did not make full use of the relationship of the components of space around problems, put forward to combine the semantic segmentation of segmentation accuracy of the optimal network DeepLabV3+ space context relationship with shock hammer. DeepLabV3+ network segmentation accuracy is improved by image segmentation and data set preprocessing, after the shock hammer and its surrounding parts are separated, the spatial context relationship is established to narrow the identification range of the shock hammer and improve its recognition accuracy. Experimental results show that image segmentation and preprocessing can improve the segmentation accuracy of DeepLabV3+ network to more than 93.4%, DeepLabV3 + network can effectively identify the normal and defective hammer, identify the recall rate can achieve 87% above, establish shock hammer with the surrounding parts of spatial context can improve the identification precision rate to more than 90%.