Due to differences in lifestyle, cultural capital and social support, foreign immigrants often have difficulty integrating into the ecology of their native communities and are limited in their space for development. To solve this difficulty, this paper applies the principle of regularization to obtain a logistic regression model by categorizing the factors affecting the social integration of foreign immigrants. The algorithms of log-likelihood function and negative Hessian matrix are used to optimize the parameters of the model, construct the multivariate logistic regression model based on the social integration of foreign immigrants, and analyze the regression results among various factors. The success rate of foreign immigrants’ local integration is higher when the immigration-related system is more perfect, the foreign immigrants’ cultural identification with the local area is higher, the cognitive deviation between foreign immigrants and locals is smaller, and the community integration structure is more appropriate. The highest correlation between the factors affecting the social integration of foreign immigrants is the formation of ethnic networks that are not embedded in the community by foreign immigrants who “embrace the group”, and the cognitive bias of local residents towards foreign immigrants, with a correlation coefficient of 0.9214, and the correlation coefficients of the rest of the indicators are less than 0.9. This paper classifies the migrants into “migrants of work nature” and “migrants of employment nature” in accordance with the purpose of their migratory activities. In this paper, according to the purpose of migration, migrants are classified into four categories: “work migration, study migration, investment migration and shelter migration”, and the results of the multivariate logistic regression analysis are credible.