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Aiming at the problems of multi workpieces stacking occlusion in unstructured scenes, a stacked workpiece recognition and location algorithm based on multi-scale feature attention Yolact network is proposed. Firstly, the proposed algorithm adds multi-scale fusion and feature attention mechanism to the mask template generation branch of Yolact network to improve the quality of network prediction stacking workpieces mask, designs a target detection module based on expansion coding to enhance the adaptability of the network to stacking workpieces with different scales, and constructs a multi-scale feature attention Yolact network. Secondly, using the constructed multi-scale feature attention Yolact network to predict the mask and bounding box of the stacked workpieces, generate the minimum circumscribed rectangle of the stacked workpieces mask, and determine the grab point and rotation angle of the target workpieces according to the mask bounding box and the minimum circumscribed rectangle of the mask. Finally, a visual robot workpieces sorting system is developed based on the stacking workpieces recognition and positioning algorithm. The experimental results show that the recognition accuracy of the proposed model in the two tasks of bounding box regression and mask prediction is improved, and the success rate of stacking workpieces sorting by the robot workpieces sorting system is 97.5%.