Abstract: Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production. It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hot-rolled strip. Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications; besides, the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process. In order to solve these problems, this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest (gcForest) framework. According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production, a three-dimensional continuous time series process data sampling method based on time–temperature–deformation was designed. The basic information of strip steel (chemical composition and typical process parameters) is fused with the local process information collected by multi-grained scanning, so that the next link’s input has both local and global features. Furthermore, in the multi-grained scanning structure, a sub sampling scheme with a variable window was designed, so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure, allowing the cascade forest structure to be trained normally. Finally, actual production data of three steel grades was used to conduct the experimental evaluation. The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance, ease of parameter adjustment, and ability to sustain high prediction accuracy with fewer samples.