The thermal behavior of silicon carbide (SiC) power modules is greatly influenced by the material properties of the SiC chip and packaging materials under high-temperature conditions. This paper proposes a novel intelligent thermal model for multi-chip SiC modules using a convolutional neural network (CNN), accounting for temperature-dependent material properties. Taking advantage of convolutional kernels to extract inter-data features, the intelligent thermal model (ITM) can capture the thermal cross-coupling (TCC) effect between chips, providing greater accuracy compared to traditional neural networks. Moreover, ITM is developed by training on a dataset generated through finite element model (FEM) low-cost simulations that consider temperature-dependent effects. Experimental validation demonstrates that the ITM significantly enhances computational efficiency, with calculation errors within 1 ℃, showing superior performance in estimating junction temperature at high-temperature conditions.