In recent years, due to the continuous optimization of network structure and the emergence of large-scale data, Convolutional neural network has made breakthroughs in a series of applications of computer vision. Based on this, the Convolutional neural network is improved and optimized. The improved convolutional neural network is introduced into image Object detection and scene recognition, and image object detection is carried out by combining sliding window fusion and Convolutional neural network. The image scene recognition model is constructed by using potential object area recognition and Convolutional neural network Transfer learning. Using different data sets to verify the algorithm, the research results show that in Group1 and Group2, the error rate of the multi column convolutional neural network fused by sliding window is reduced by about 25% compared with the single column convolutional neural network. As the group with the smallest decrease in error rate, Group3 also achieved a 9% decrease in error rate. The fitness rate of object detection algorithm is gradually stable after 7 runs, reaching about 9.8%, and its operation effect is obviously better than other algorithms. The multi column convolutional neural network fused by sliding window is more adaptive to the training data set, and gets better recognition effect in the algorithm operation. However, the image scene recognition model based on potential object area recognition algorithm and Convolutional neural network has good convergence. The average recognition time for image scenes is 1.5356s. The recognition speed is fast and stable, which can effectively solve the problem of multi-scale image scene recognition.

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