Research on dynamic motion pattern recognition and analysis algorithm based on artificial intelligence

This paper focuses on the dynamic motion pattern recognition and analysis algorithm based on artificial intelligence, and proposes an innovative motion pattern recognition method, which combines fast Fourier transform (FFT) and fusion random forest model. Firstly, the motion signal is efficiently transformed from the time domain into the frequency domain through the application of the fast Fourier transform, enabling the extraction of key frequency characteristics, thereby enhancing the recognition capability of the motion pattern. The accuracy and stability of the model are subsequently enhanced by integrating the outputs from multiple decision trees, leveraging the power of the fusion random forest algorithm. In the random forest decision algorithm, the integrated learning strategy is introduced, which realizes the deficiency of single decision tree and further improves the classification performance of motion patterns. The experimental outcomes demonstrate that this method exhibits outstanding performance across various motion pattern recognition tasks, coupled with remarkable real-time capabilities and precision. This research provides a new idea and method for the field of motion pattern recognition, which has important theoretical value and practical application prospect.