Abstract: Text classification of transmedia knowledge involves many types of data, such as text, image, video, etc. The heterogeneity and heterogeneity of data increase the complexity of classification. Aiming at the problem that it is difficult to find accurate data in a large number of cross-media knowledge texts, an algorithm of cross- media knowledge text classification based on multi-modal information is proposed. The TF-IDF(Term Frequency- Inverse Document Frequency) algorithm is used to filter the stop words in the processing text, extract the text features, and integrate them with the image text features. By using naive Bayes classifier, the classification of cross-media knowledge text is determined and realized. Experimental analysis shows that the proposed text classification algorithm significantly improves the performance and efficiency of cross-media knowledge text classification, and makes the classification results more accurate, with the accuracy rate up to 95. 12% and the missing rate remaining below 10%.

Key words: text classification, term frequency-inverse document frequency ( TF-IDF) algorithm, bilinear pooling, naive bayesian classifier