In the context of big data of public health events, the inheritance and innovation of fine art creation becomes a new opportunity and challenge. In this paper, based on the art image data collection, we establish a convolutional neural network model with migration learning and inverse algorithm for the recognition of art creation under the big data of public health events. The experimental results show that the visual image analysis algorithm can classify paintings with an accuracy of more than 99%, which has good painting recognition effect and classification ability. The migration learning model achieves a training accuracy of over 97% at 50 training rounds and also performs well on the test set with an accuracy of 96. 97%. Using the neural network model of art creation established in this paper for intelligent innovation of art images, with the increase of model settings, it can guarantee the recognition of art creation images between 60% and 80%, and with the increase of iterations, the effective linearity keeps stabilizing and the innovation rate keeps increasing, up to 79.0%. In this paper, the established methods are introduced into Lenet-s model, GoogleNet model and ResNet model for experiments, and it can be seen that the training sets1 of the three models have greater than or equal to 99.8%, 99.6% and 99.4%, respectively, and the training sets2 of the three models have 99.4%, 99.2% and 99.4%, respectively, which proves the neural network model of this paper validity.

Jun Wu. Heritage and Innovation of Art Creation in the Context of Big Data and Public Health Events. Frontiers in Art Research (2023) Vol. 5, Issue 7: 57-62. https://doi.org/10.25236/FAR.2023.050711.

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