Traditional pull-up assessments are typically conducted manually, a process that lacks objectivity and is both time-consuming and labor-intensive. To overcome these limitations, it is essential to develop an automated model for evaluating the correctness of pull-up movements. However, due to the absence of publicly available datasets, we constructed a custom pull-up action dataset consisting of videos recorded from both professional athletes and regular college students. In response to the limitations of existing action recognition models, this paper proposes an Adaptive Multi-dimensional Fusion Graph Convolutional Network (AMF-GCN), which comprises two key components: an Adaptive Graph Convolutional Module (AGCN) and a Multi-dimensional Fusion Module (MF). The AGCN module adaptively modifies the graph topology based on sample-specific data, enabling it to capture complex relationships among various joints. The MF module fuses information from multiple feature dimensions, thereby enhancing the model’s flexibility and generalization capability. Extensive experimental comparisons demonstrate that our method achieves an accuracy of 97.33 \(\%\) , significantly outperforming several state-of-the-art action recognition models, thus validating its superior performance and effectiveness.