With the development of artificial intelligence technology and deep learning, the application of related network algorithm models in human motion posture recognition is becoming increasingly widespread. To address low accuracy and complex networks in traditional algorithms for human motion posture recognition, this study proposes a method based on particle swarm optimization to improve the backpropagation neural network for recognizing and analyzing human motion posture. The model first extracts key nodes from image data through improved OpenPose, performs viewpoint transformation, and uses an optimized backpropagation neural network to recognize relevant human postures. The experiments showed that the improved OpenPose algorithm had high accuracy in extracting key nodes. When the viewing angle was 108°, the error value generated by viewing angle conversion was the smallest and the accuracy was the highest. The Schaffer function of the backpropagation neural network model optimized by particle swarm optimization and small batch gradient descent converged after 60 and 99 iterations, respectively. The Griebank function converged after 78 and 98 iterations. The particle swarm optimization-based backpropagation neural network achieved an average improvement of 20%, 22%, 16%, and 12% in recognition rates for four different human motion postures compared to other algorithms. The results show that the particle swarm algorithm-improved backpropagation neural network has higher computational efficiency and better accuracy in human motion posture recognition.