The traditional single fuzzy classifier method, which uses fixed de-fuzzification rules, is easy to case the problem of text ambiguity in emotional data. To solve the problem, a text classification method based on deep neural networks and fuzzy rules is proposed. The method is divided into two main stages. In the first stage, we use fuzzy rule formation algorithm and different fuzzy norms, feature extraction method (word bag method, word embedding vector), and feature subset selection method based on association to prepare features, so as to train multiple fuzzy classifiers. Then, we fuse classifiers to identify ambiguous instances. In the second stage, disambiguation instances are sorted out, a second training set is generated, and KNN is used to classify new disambiguation instances. Compared with the current advanced methods, the proposed method has better classification performance in most cases, and the Wilcoxon rank test is statistically significant.