The cultivation and breeding of rice in Hainan， one of the primary tropical regions in China， play a crucial role in meeting the country's demand for this essential food crop. Currently， there are several challenges in monitoring rice cultivation in the tropical region of Hainan， including limited automation， excessive workload， and low accuracy. In this study， we selected Haikou City in Hainan Province as our experimental area. By utilizing high-resolution multi-spectral satellite remote sensing images such as Jilin-1， Beijing-2， WorldView， and Gaojing-1 along with field verification data， we established a comprehensive database consisting of multi-source and multi-scale samples to accurately identify rice planting areas within the tropical region of Hainan. We employed the DeepLab-V3+ convolutional neural network model for training purposes and proposed an intelligent remote sensing interpretation method specifically tailored for identifying rice planting areas within the tropical region. Experimental results demonstrated that our approach achieved an impressive accuracy rate of 81.9% with a recall rate of 86.7% when extracting rice intelligently based on the DeepLab-V3+ convolutional neural network model. These findings highlight that by training a convolutional neural network model using our interpretive sample database， it becomes possible to accurately extract regions where tropical rice is cultivated from high-resolution multi-spectral remote sensing imagery—a methodology that can serve as a valuable reference for future studies on extracting information related to tropical rice cultivation.