As a key thermal-state indicator of the iron ore sintering process, the content of ferrous oxide (FeO) in the finished sinter is directly related to product quality. Based on the massive data of sintering process, the data-driven soft sensor model provides a good choice for real-time FeO content detection. However, the complex characteristics of the data, including dynamics, nonlinearity, and multisource heterogeneity, are still the main obstacles to improving the modeling accuracy. To solve this problem, in this article, a multisource information fusion autoformer (MIF-Autoformer) model is introduced. First, feature-level information fusion and data-level information fusion are implemented based on the MIF strategy. Then, the comprehensive information of the sintering process is fed to the downstream autoformer model in a serial manner, which not only improves the information capacity but also provides additional prior information about the FeO content grade. This is helpful for autoformer to capture the complex temporal distributions in the sintering process. Finally, the proposed model is applied to a real sintering plant. Experimental results show that the hybrid image features provided by the MIF strategy have a general optimization effect on different competitive models, and MIF-Autoformer exhibits the lowest prediction error on the test set.