The invention discloses a multidimensional weighted 3D recognition method for dynamic gestures. At the training stage, firstly, standard gestures are segmented to obtain a feature vector of the standard gestures; secondly, coordinate system transformation, normalization processing, smoothing processing, downsampling and differential processing are performed to obtain a feature vector set of the standard gestures, weight values of all joint points and weight values of all dimensions of elements in the feature vector set, and in this way, a standard gesture sample library is constructed. At the recognition stage, by the adoption of a multidimensional weighted dynamic time warping algorithm, the dynamic warping distances between the feature vector set Ftest of the gestures to be recognized and feature vector sets Fc =1,2,...,C of all standard gestures in the standard gesture sample library are calculated; when the (m, n)th element S(m, n) of a cost matrix C is calculated, consideration is given to the weight values of all the joint points and the weight values of all the dimensions of the elements, the joint points and coordinate dimensions making no contribution to gesture recognition are removed, in this way, the interference on the gesture recognition by joint jittering and false operation of the human body is effectively removed, the anti-interference capacity of the algorithm is enhanced, and finally the accuracy and real-time performance of the gesture recognition are improved.