The invention relates to a visual-angle-independent video three-dimensional human body posture recognition method. The method comprises the steps: 1, a virtual data generation stage: generating a two-dimensional/three-dimensional data tuple after synthesizing virtual camera parameters based on any human body posture data set containing three-dimensional marks; step 2, a model training stage: utilizing the generated two-dimensional/three-dimensional data tuples to train a modular neural network first module used for obtaining a model with camera visual angle generalization capability and a modular neural network second module used for obtaining a model capable of protecting inter-frame action continuity respectively; and 3, an unconstrained video reasoning stage: for any unconstrained acquired video, predicting by utilizing the multi-module deep neural network obtained by training in the step 2 to obtain a three-dimensional human body posture recognition result. Compared with the priorart, the method is based on a modular neural network combination training method, and the generalization ability of three-dimensional human body posture recognition is effectively improved.