The invention discloses a deterministic learning theory based gait recognition method irrelevant to a visual angle, and belongs to the technical field of pattern recognition. The method comprises the following steps: preprocessing; extracting characteristics; on the basis of extracted gait characteristics, dynamically carrying out neural network modeling and identification to a gait system at different visual angles in a training set; establishing a literal neural network; and constructing a dynamic estimator, and realizing the accurate classification recognition of test modes according to a least error principle by utilizing differences among gait modes on gait system dynamics at different visual angles. The dynamic local accurate modeling and identification of the human body gait system at different visual angles can be realized, meanwhile, gait modes at different visual angles are formed into a uniform training gait mode library, a problem of a traditional method that corresponding training sets need to be independently constructed for gait modes at different visual angles can be overcome, the gait recognition irrelevant to the visual angle is realized, and the invention exhibits higher robustness and practicality.