Accurate wind speed forecasting is critical to the exploitation and utilization of wind energy. In this paper, a novel wind speed multi-step prediction model is designed based on the SSA (Singular Spectrum Analysis), EMD (Empirical Mode Decomposition) and CNNSVM (Convolutional Support Vector Machine). In the SSA-EMD-CNNSVM model, the SSA is used to reduce the noise and extract the trend information of the original wind speed data; the EMD is used to extract the fluctuation features of the wind speed data and decompose the wind speed time series into a number of sub-layers; and the CNNSVM is used to predict each of the wind speed sub-layers. To investigate the prediction performance of the proposed model, some models are used as the comparison models, including the SVM model, CNNSVM model, EMD-BP model, EMD-RBF model and EMD-Elman model. According to the prediction results of the four experiments, it can be found that the proposed model can have significantly better performance than the seven comparison models from 1-step to 3-step wind speed predictions with the MAPE of 42.85% average performance promotion, MAE of 39.21% average performance promotion, RMSE of 39.25% average performance promotion.