Oil painting image style recognition based on ResNet-NTS network

With the improvement of economic level, cultural and artistic pursuits have become the new life style of people. For painting exhibition style appreciation, the study uses deep learning and neural networks to classify and recognize the style identification of oil painting images. Firstly, the classification technology of residual networks is used to collect features and enhance regions in images of Western oil painting art. Secondly, the residual network is optimized and improved. Finally, attention mechanism and improved activation function are introduced to achieve the recognition effect of oil painting image style. Through comparative experiments, the results show that the improved residual network has an accuracy of 69.38% in style recognition of oil painting datasets. After optimizing with cosine annealing decay strategy, the network testing system model has the highest recognition accuracy of 74.41% for oil painting style data. Finally, the residual network and the network test system are respectively used in the style recognition of Xizang Thangka art dataset, and the accuracy of style recognition of the improved residual network and the network test system is 87.39% and 95.28%, respectively. Therefore, it proves that the residual network and network test system model based on the residual network and network test system model have superiority in the effect of oil painting style recognition, and improve technical support for art dissemination and management.