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In recent years, for China, animation industry is a relatively new and mature emerging national sunrise industry after animation industry, which appears on the world stage more and more frequently and is widely concerned and valued by people from all over the world. Therefore, this paper innovatively uses the convolutional neural network algorithm to innovate the color matching generation of animation characters and improve the traditional technology of color matching for animation characters. In this paper, we mainly use Generative Adversarial Network (GAN), Deep Convolutional Generative Adversarial Network and VGG model, and multiscale discriminator theory and use ACGAN research method. And we study this paper's innovative LMV-ACGAN research method, and we have come to the conclusion that other models have higher collapse rate than this model; this model has higher color matching of anime characters. Color matching improves with the increase of convolutional neural network utilization, etc. Moreover, superior and minor reviews of this study are provided to make later researchers understand this study more rationally and objectively. [ABSTRACT FROM AUTHOR]

In recent years, China's sports industry has achieved good development, but the efficiency of athletes in the training process is difficult to have scientific guarantee. How to use scientific algorithm and data mining technology to accurately guide the sports training process has become a hot spot. Based on this, this paper studies the gait recognition model of sports training based on convolutional neural network algorithm. First, this paper analyzes the research status of gait recognition in the process of training and optimizes and improves the deficiencies in sports training. Then, the convolutional neural network algorithm and data mining technology are optimized and analyzed in the gait recognition model. Finally, the experimental results show that the convolutional neural network algorithm can realize the recognition and model reconstruction of athletes' gait in the training process and can make the optimal strategy according to the gait differences of different athletes in the training process, and the recognition accuracy of athletes' gait can reach more than 97%. [ABSTRACT FROM AUTHOR]