In order to further realizethe effect ofscapula dyskinesison weightlifters under the action of convolution neural network algorithm and biological image in weightlifting, in this study, the effect of scapula dyskinesis in weightlifting is studied by the way of weight lifting biological image and convolution neural network algorithm.The stability, strength and speed of the scapula under the snatch and the stability, strength and speed of the scapula under the clean and jerk are quantified to determine the effect of the dyskinesisof the scapula on the weightlifter under the action of the biological imageand the convolution neural network algorithm. In this study, the biological images of each stage in the process of weightlifting are decomposed. Convolution neural network algorithm is used for data analysis, so as to realize the support and function of scapula for weightlifting. The results show that the weight lifting optimization effect based on the biological image and convolution neural network algorithm ofscapula dyskinesisis the most significant. The stability, strength and speed of scapula of athletes have been improved obviously. It can be seen that the research method based on the biological image of weightlifting and convolution neural network algorithm has a good help for weightlifters.Conclusion: The effect of scapula dyskinesis under the influence of biological image and convolution neural network algorithmis studied. The stability, speed and strength of scapula of athletes in two different lifting ways are studied. The research shows that the method in this study has a very significant optimization and improvement for the weightlifting effect of athletes under different weightlifting methods. Through this study, it also shows that the effect improvement of weightlifting needs to be studied in many aspects, and a single level is unable to provide substantive help. This study greatly improvesthe realization of scapular dyskinesis.
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