[Objective] Once the width of the convolution kernel is the same as the dimension of the word vector, the convolution layer will have too many parameters. The sparse connection of convolution operation, the spatial invariance, and the channel specificity of convolution are not suitable for text tasks. This paper will address these issues. [Methods] We proposed a sentiment analysis model for texts based on patch attention mechanism and Involution. The model first transformed the single-word vector after word segmentation and transformed the one-dimensional word vector into n×n word matrix blocks. Then, we spliced the word matrix blocks of multiple words in the sentence into a sentence matrix. Third, the patch attention mechanism layer enhanced the sentence matrix’s context relevance and position order information of text features. Fourth, we used the involution with spatial specificity and channel invariance to extract the sentence matrix features. Finally, we used the full connection layer for text sentiment classification. [Results] We examined the proposed model with three public data sets waimai_10k, IMDB, and Tweet. Its classification precision reached 88.47%, 86.22%, and 94.42%, respectively, which were 6.47%, 7.72%, 9.35% and 1.07%, 1.01%, 0.59% higher than Bi-LSTM model in word vector convolution network and recurrent neural network. [Limitations] The classification accuracy of this model on large datasets is not as high as on small and medium-sized datasets. [Conclusions] The proposed model solves the problems of excessive parameters, sparse connection of convolution operation, spatial invariance, and channel specificity of convolution, which yield better performance than the traditional convolution models.