Unlike traditional multi-modal fusion methods that are predominantly image-based， production data in industrial manufacturing are primarily structured data， with a small amount of image features. However， both types of heterogeneous data reflect the core parameters of shale gas. Due to the significant difference in data dimensions， it is challenging to achieve feature fusion of heterogeneous data. Additionally， there is heterogeneity among the stratified structured data， leading to substantial errors in predicting core parameters using conventional deep learning methods. To address these issues， this paper proposes a multi-modal fusion algorithm for heterogeneous data （MFH）. Firstly， a multi-modal fusion strategy for heterogeneous data is designed to align， extract， and merge features of scanning electron microscopy and logging parameters under the same depth labels. Secondly， a mechanism for drawing heterogeneous data features closer is constructed to create positive sample pairs， enabling the model to learn about the strong heterogeneity between stratums in the same work area and the lateral nonlinear relationships. Finally， a method for exchanging features of heterogeneous data is introduced to solve the matching problem between abundant logging data and scarce electron microscope images， achieving accurate and continuous prediction of core parameters. Experimental results， compared with predictions from mainstream deep models， prove the practicality， effectiveness， and extensibility of the proposed scheme.