Spatial transcriptomics (ST) reveals the intricate dynamics of cell regulation and gene expression but is often limited by high costs. Leveraging artificial intelligence to predict spatial gene expression from histological images provides a more economical alternative. However, current approaches struggle to fully capture deep-level information embedded in histological images. In this study, we introduce DCGT, a deep learning method that combines Dynamic Convolutional networks and Graph Transformer to dynamically uncover potential molecular patterns within histological images. This approach bridges a connection between detailed histological image features and spatially resolved gene expression. Extensive evaluations using four different spatial transcriptomics datasets highlight the remarkable efficiency of DCGT for predicting spatial gene expression. Furthermore, DCGT effectively uncovers spatial contexts and accumulation markers within defined tissue regions.