Aiming at the problems of low accuracy, poor real-time performance and insufficient feature extraction in existing text sentiment analysis methods, a serial hybrid model based on Bi-directional Long Short-Term Memory neural network and Convolutional Neural Network (BiLSTM-CNN) was constructed. Firstly, the context information was extracted from the text by using Bi-directional Long Short Term Memory (BiLSTM) neural network. Then, the local semantic features were extracted from the context information by using Convolutional Neural Network (CNN). Finally, the emotional tendency of text was obtained by using Softmax. Compared with single models such as CNN, Long Short-Term Memory (LSTM) and BiLSTM, the proposed text sentiment analysis model increases the comprehensive evaluation index F1 by 2.02 percentage points, 1.18 percentage points and 0.85 percentage points respectively; and compared with the hybrid models such as LSTM and CNN (LSTM-CNN) and parallel features fusion of BiLSTM-CNN, the proposed text sentiment analysis model improves the comprehensive evaluation index F1 by 1.86 percentage points and 0.76 percentage points respectively. The experimental results show that the serial hybrid model based on BiLSTM-CNN has great value in practical applications.