Iron ore resources are the foundation of our economic development and social progress. In the process of iron ore mining, the rapid and accurate determination of iron ore grade has an important influence on the mining decision and economic benefit. Hyperspectral imaging technology has the advantages of wide image coverage and high accuracy and has been widely used in ore classification and composition inversion. However, the band range of existing hyperspectral imaging sensors mainly includes visible and shortwave infrared (Vis-SWIR) and near-infrared (NIR). The two data types are mostly acquired independently, lacking continuity, and the accuracy of the model built with single data is often low. Therefore, the fusion of spectral data obtained by multiple sensors can effectively solve the problems of the small band range of a single sensor and few bands containing target characteristics and improve the accuracy of iron ore grade inversion based on hyperspectral imaging technology. In this study, Pika L and Pika NIR-320 hyperspectral imagers were used to collect imaging spectral data of Anshan iron ore in Vis-SWIR and NIR bands, respectively, and a spectral series fusion method based on mutual information (MI) was proposed. Firstly, the two groups of spectral data were preprocessed. Then, mutual information is calculated on the processed data to conduct a series fusion of spectral data. Finally, Vis-SWIR, NIR, and spectral data based on the series fusion of different bands were used as data sources to establish RBF neural network grade inversion models, and the accuracy and precision of the models based on spectral data before and after fusion were used as the discrimination index of the effectiveness of the fusion algorithm. The results show that the accuracy and precision of the model built after series fusion of spectral data is higher than that built using Vis-SWIR and NIR spectral data alone. Compared with the spectral data based on series fusion of other bands, the accuracy and precision of the model established based on the mutual information calculation of series fusion spectral data at 959.89 nm are the highest, R2 0.88, RPD 2.97, RMSE 4.464, MAE 3.32. This study proposes a new idea for multi-sensor spectral fusion, which has practical significance for the application of imaging spectrum technology in the inversion of iron ore grade.