The current deep learning models in the field of compilation optimization generally perform single-task learning and fail to use the correlation among multiple tasks to improve their overall compilation acceleration effect. For this reason, a compilation optimization method based on multi-task deep learning is proposed. This method uses the graph neural network (GNN) to learn program features from the abstract syntax trees (ASTs) and control data flow graphs (CDFGs) of the C program and then predicts the initiation interval and loop unrolling factor for the software pipelining of the HX digital signal processor (HXDSP) synchronously according to program features. Experimental results on the DSPStone dataset show that the proposed multi-task method achieves a performance improvement of 12% compared with that of the single-task method.