Aiming at the problem of supply-demand matching of online reading, an analysis method of children’s online reading behavior oriented for family education has been put forward. The data-based classification method is constructed to classify the sample population by statistical methods, and the traditional index classification is carried out by using K-medoids clustering and logistic regression analysis. The matching degree of population classification is discussed through comparison. R language and Mplus are used to analyze the data for the objective classification of the sample data set. Based on the reading response behavior of children’s online reading users, a differential item functioning (DIF) test of socioeconomic status is carried out. At the same time, the population is divided by traditional economic classification indicators to carry out a DIF test and explore the differences in the reading ability of different classification groups. By comparing the results of the two grouping methods, the main family socioeconomic status factors affecting reading performance are explored and targeted countermeasures are put forward. The experimental results show that when analyzing children’s online reading behavior, using machine learning algorithms such as cluster analysis, logistic regression analysis, and so on can get consistent results and then using the DIF test to explore the responses of category groups can effectively distinguish group differences.