The digital economy is a new economic form taking data as an important production factor and digital and intelligent technology as a driving force for transformation. The core idea is to extract and fuse the knowledge implicit in data and transform it into intelligence to drive the transformation of traditional manufacturing industries, and one of its key technologies is multi-modal data fusion. In this paper, an improved MPGA-ACO-BP algorithm is proposed, and combined with an improved entropy-weighted TOPSIS method comprehensive evaluation system, which effectively solves the problem of “data scale inconsistency” between modal data leading to difficult model fusion and fusion accuracy. Finally, the validity of the theory and methods of this paper are verified using the example of multi-modal data fusion tool wear prediction in an intelligence workshop. By distilling the corresponding evaluation metrics inductively, the improved comprehensive evaluation system in this paper can also be extended to different production control scenarios to provide them with the corresponding integration information, which has a certain practical value.