This paper proposes a baseball pitching action recognition algorithm based on a spatiotemporal graph convolutional neural network and constructs an error action correction algorithm on this basis. The dynamic skeleton model ST-GCN is used to combine the positional information of human movement with the temporal dynamic information. The action contour sequence is extracted to determine the funding for the erroneous action. Finally, the machine learning method is used to realize the adaptive corrective analysis of the erroneous action. Example analysis shows that the action correction algorithm proposed in this paper improves the recognition accuracy by 20.78%, 16.67%, and 9.11%, 9.73% in the two datasets, and the pitching accuracy of the experimental group is 12.5% higher than that of the control group, and the standardized degree score of the pitching technical action is 1.1 points higher than that of the control group. Therefore, the practical effectiveness of the pitching action identification and correction method in this paper has been effectively verified.