Abstract: With the emergence of three-dimensional data in the network,the advantages of triadic concept analysis (TCA) have been reflected gradually.As a relatively new field,TCA has a bright prospect.This paper proposed a text classification algorithm based on TCA,which is a novel idea and a development of TCA in application aspect.The main idea of this algorithm is firstly preprocessing the dataset so that we can convert it into triadic context,meanwhile extend the binary relation in the context to a fuzzy value between 0-1 which represents membership degree about attribute for object under certain conditions.Based on this,we can build triadic concepts and utilize it to express the ternary relation among text,term and category.Then,combined with the approach degree in fuzzy theory,we can analogize the similarity formula of triadic concepts,accordingly calculate the training set’s similar value about triadic concept for a new text.Compared to support vector machine(SVM),K-nearest neighbor (KNN),convolution neural network (CNN) algorithm and classification based on formal concept analysis model,the results indicate that the proposed model in specific dataset is effective and achieves a better performance.

Key words: Triadic concept analysis,Triadic concept,Fuzzy theory,Text classification,Triadic concept similarity