In multiple fields such as intelligent monitoring and human-computer interaction, the application of action simulation recognition is indispensable. This article focuses on a digital media motion simulation recognition model based on Unity3D technology, aiming to improve the accuracy and stability of motion recognition through motion capture technology. By constructing an efficient, flexible, and highly interactive action recognition model, this article not only explores the construction and configuration of the Unity3D environment, but also analyzes action capture and data preprocessing, the construction of action recognition models, and the effectiveness of view invariance. This article first introduces the setup of the Unity3D environment, including the installation of the editor, project creation and configuration, and the use of the scene editor. The import of character models, motion capture and animation, as well as the writing of interaction logic, provide a solid foundation for motion simulation. Furthermore, this article explores the cleaning and standardization of motion capture data, as well as the construction of motion recognition models. Long short-term memory networks are used to process temporal information, while principal component analysis is used for feature optimization. View invariance training improves the recognition performance of the model through multi perspective feature learning and fusion. The experimental results show that the action simulation recognition model under Unity3D outperforms traditional techniques in terms of recognition accuracy and stability. Specifically, the recognition accuracy of the model remains stable between 90.45% and 99.68%, with the highest stability reaching 99.8%. The comparative analysis before and after view invariance training further reveals the positive impact of training on improving model response time and interaction delay.