Understanding the spatial distribution, temporal dynamics, and driving factors of soybean cultivation is critical for yield estimation, agricultural planning, and national food security. However, high-resolution, long-term, and nationwide datasets of soybean cultivation in China remain scarce. This study developed a 30-m resolution dataset of soybean in China from 2000–2022 using multi-source data (ChinaSoyA30m), and analyzed the spatiotemporal dynamics and driving forces of soybean cultivation. The phenological characteristics of major crops across China were evaluated to generate training samples for supervised classification. Gap statistics, K-means clustering, and spectral angle mapping were employed to enhance classification reliability. A supervised classification approach was implemented on Google Earth Engine (GEE) using dense Landsat data to produce annual soybean maps. ChinaSoyA30m demonstrates competitive performance compared to six existed soybean datasets, with strong correlations with provincial, prefectural, and county statistics (R²=0.95, 0.89, and 0.80), and the F1 scores validated against ground truth data were 70.16, 80.40, and 78.38%. Since 2000, the soybean planting area has exhibited a fluctuating upward trend with distinct regional characteristics. Northern China emerged as the primary production area, characterized by a stable planting centroid and small spatial variation. The primary driver of soybean area dynamics was the “value added of primary industry”, while gross power of agricultural machinery was a significant factor in North China, highlighting regional differences in driving mechanisms. This study provides the first long-term, high-resolution soybean planting dataset for China and offers valuable insights into the sustainable development of soybean cultivation.