Aiming at the problems that simple Recurrent Neural Network (RNN) cannot memorize information for a long time and single Convolutional Neural Network (CNN) lacks the ability to capture the semantics of text context, in order to improve the accuracy of text classification, a sentiment analysis model G-Caps (Gated Recurrent Unit-Capsule) was proposed, which combines Gated Recurrent Unit (GRU) and capsule features. First, the contextual global features of the text were captured through GRU in order to obtain the global scalar information. Second, the captured information was iterated through the dynamic routing algorithm at the initial capsule layer to obtain the vectorized feature information representing the overall attributes of the text. Finally, the features were combined in the main capsule part to obtain more accurate text attributes, and the sentiment polarity of the text was analyzed according to the intensity of each feature. Experimental results on the benchmark dataset MR (Movie Reviews) showed that compared with the CNN + INI (Convolutional Neural Network + Initializing convolutional filters) and CL_CNN (Critic Learning_Convolutional Neural Network) methods, G-Caps had the classification accuracy increased by 3.1 percentage points and 0.5 percentage points respectively. It can be seen that the G-Caps model effectively improves the accuracy of text sentiment analysis in practice.