In earth sciences the rock microscopic data collection process is both labor-intensive and inefficient, which have a negative impact on research cost/reliability and open data sharing. Additionally, rock heterogeneity and variation in data collection methods typically result in small-scale datasets—this poses a significant challenge to deep learning frameworks that rely on large-scale datasets for training. To address this issue, we investigate how transfer learning can facilitate information sharing across different rock types and enhance model performance in tasks such as mineral identification and intelligent interpretation. By compiling thin section image datasets, taking in diverse rock sampling regions, rock types, mineral compositions, viewed under varying viewing modes, we delve into the mechanisms of transfer learning across different observational targets and methods, focusing on the deep representation of geological information. Our findings not only highlight the pivotal role of transfer learning in promoting information sharing and improving model performance within the field of geosciences, but also lay a foundation for the automatic and intelligent integration of geological insights. According to experimental results, transfer learning led to significant accuracy improvement, from 53.3% to 98.73%, in intelligent interpretation task, and a nearly 10% improvement in mineral identification task. These results convincingly showcase the great potential of transfer learning in addressing practical problems in geology as well as enhancing model generalization, model performance, and model stability.