Feature extraction is the critical step in power quality disturbances recognition, while the traditional methods combining the mathematical manipulations and shallow neural networks cannot extract the features automatically. Therefore, the paper proposes a hybrid model based on the Convolutional Neural Network (CNN) and Random Forest (RF) to perform the automatic feature extraction and classification of the three phase voltage dip data. Firstly, the three phase voltage dip data is transformed to the Space Phasor Model (SPM). Secondly, CNN is used for extracting the features of the SPM. Finally, RF is applied for classification. For the acceleration of the training of CNN and the relief of over-fitting, the dropout, exponential decay of learning rate and update of weights by adaptive moment estimation are introduced. Experimental results demonstrate that the proposed method has a better generalization performance and higher classification accuracy compared to other classification methods, which provides an objective and efficient auxiliary method for voltage dip recognition. This work is supported by National Natural Science Foundation of China (No. 61662047).