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Aiming at the limitations of existing mobile robots in visual obstacle avoidance, a robot obstacle avoidance method based on deep convolutional neural network and Bug algorithm is proposed by combining deep learning algorithm and path planning technology. In this method, multi task deep convolution neural network is used to extract road image features to realize image classification and semantic segmentation; Secondly, the grid map is constructed based on the semantic segmentation results, and the image classification results are combined with the improved bug algorithm to search the optimal obstacle avoidance path; At the same time, in order to reduce redundant calculation, a feature comparison structure is designed to avoid the feature information of repeated calculation and ensure the real-time performance of the robot in practical application. The experimental results show that the proposed method effectively balances the accuracy and efficiency of multi-vision tasks, and can accurately plan a safe obstacle avoidance path to assist the robot to complete navigation and obstacle avoidance.