In the application of quantum communication networks, it is an important task to realize the optimal allocation of resources according to the current situation. For example, We need to select the optimal quantum key distribution (QKD) protocol and parameters. Traditionally, the most commonly implemented method is the local search algorithm (LSA), which costs a lot of resources. Here in this work, we propose a machine learning based scheme, in which the regression machine learning is used to simultaneously select the optimal protocol and corresponding parameters. In addition, we make comparisons among a few machine learning models including random forest (RF), K-nearest neighbor (KNN) and logistic regression. Simulation results show that the new scheme takes much less time than the LSA scheme, and the RF achieves the best performance. In addition, through the RF residual analysis, we find that the machine learning scheme has good robustness. In conclusion, this work may play an important role in promoting the practical application of quantum communication networks.