At present there lacks a way to measure home-school communication in a smart campus. Concerning the obvious identity characteristics when chatting in a smart campus, a new multi-label classification algorithm named Adaboost.ML (Multiclass, multi-label version of Adaboost based on user identity) was proposed. Firstly, the heuristic rule was added for the proposed algorithm. Then, the Adaboost.MH (Multiclass,multi-label version of Adaboost based on Hamming loss) algorithm was introduced, and the concept of dataset sharding was discarded. Finally, the single data was used as the focus of analysis, which reduced the inference time and the error caused by the edge of the time slice. The comprehensive decision-making about the relationship between the chat users was made out. The experimental results show that, compared with the heuristic algorithm based on rules, the false positive rate of the proposed algorithm is decreased by 53% while its false negative rate is reduced by 66% on the dataset of smart campus. The proposed algorithm also has good classification results on the dataset of WeChat. At present, the proposed algorithm has been applied to the smart campus project, and it can get home-school communication quickly and accurately.