The invention relates to the field of short-term wind speed prediction, and discloses a short-term wind speed prediction method based on SSA-HMD-CNNSVM model. The method includes: firstly, utilizing singular spectrum analysis (SSA) to reduce noise and extract trend information of original wind speed data; and then, carrying out deep decomposition on the wind speed data by using mixed mode decomposition, then, predicting each wind speed sub-layer by using a convolutional support vector machine, and finally, carrying out superposition on prediction results of all components, thereby obtaining afinal wind speed prediction result. Compared with a common signal preprocessing mode, noise reduction and deep decomposition processing on the wind speed time sequence can effectively reduce the influence of random fluctuation of the wind speed time sequence on a prediction result, and the accuracy and precision of wind speed prediction are greatly improved. Meanwhile, the CNNSVM can combine the advantages of a single model convolutional neural network and a support vector machine, so that the wind speed prediction method has strong generalization capability and robustness, and can be appliedto wind power plant wind speed prediction on a large scale.