For the loss of much semantic information in dimension reduction of text features when using pooling layer of the traditional Convolutional Network (CNN) model, a Convolutional Neural Network model based on Singular Value Decomposition algorithm (SVD-CNN) was proposed. Firstly, an improved Active Learning algorithm based on Density Center point sampling (DC-AL) was used to tag samples contributing a lot to the classification model, obtaining a high-quality model training set at a low tagging cost. Secondly, an SVD-CNN barrage text classification model was established by combining SVD algorithm, and SVD was used to replace the traditional CNN model pooling layer for feature extraction and dimension reduction, then the barrage text classification task was completed on these bases. Finally, the model parameters were optimized by using Partial Sampling Gradient Descent algorithm (PSGD). In order to verify the effectiveness of the improved algorithm, multiple barrage data sample sets were used in the comparison experiments between the proposed model and the common text classification model. The experimental results show that the improved algorithm can better preserve semantic features of the text, ensure the stability of training process and improve the convergence speed of the model. In summary, the proposed algorithm has better classification performance than traditional algorithms on multiple barrage texts.