Existing methods utilized single words as text features.Some words contain multiple meanings,and it is difficult to distinguish its specific classification according to a single word,which probably affects the accuracy of the text classification.Propose a framework based on Words in pairs neural networks (WPNN) for text classification.Words in pairs include all single word combinations which have a high mutual association.Mine the crucial explicit and implicit Words in pairs as text features.These words in pairs as a text feature are easily classified.The words in pairs are utilized as the input of the neural network,which provides a better classification ability to the model,because they are more recognizable than the single word.Experimental results show that our model outperforms five benchmark algorithms.