Abstract: Phenotypic data serve as the foundation for effective monitoring of fish species. Currently, fish classification heavily relies on expertise from relevant professionals, leading to issues such as low efficiency, high errors, potential damage to fish bodies, and susceptibility to subjective factors affecting data quality. In this study, we developed a simplified device for acquiring three-dimensional models of fish, pioneering the creation of a dataset comprising authentic three-dimensional fish models. By leveraging the Point Transformer algorithm, we can rapidly, efficiently, and accurately extract phenotypic features from three-dimensional fish bodies, enabling precise classification of different fish species. A total of 110 authentic fish three-dimensional models were collected in this research, resulting in 1650 experimental samples after preprocessing, rotation enhancement, and downsampling operations on the acquired 3D models. Subsequently, through classification training and validation using the Point Transformer network and two comparative networks for three-dimensional classification, the results indicate that the proposed Point Transformer method outperforms the two comparative networks, achieving an overall classification accuracy of 91.9%. Simultaneously, an effective evaluation of the utilized three-dimensional classification networks was conducted, demonstrating the meaningful classification of the three models for five authentic fish species models. The Point Transformer model exhibited the highest ROC curve accuracy and the largest AUC area, proving its effectiveness in classifying three-dimensional fish datasets. This study presents a method for accurately classifying three-dimensional fish models, offering a new technological approach for intelligent monitoring of fisheries resources in the future.