Abstract: Over the past several decades, the consumers’ demand for soybeans has grown rapidly in China, resulting in a significant increase in the gap between production and demand. Therefore, increasing the total soybean output is of critical importance to ensure food security. Given that it is difficult to increase the total area of cultivated land in China, improving soybean yield per unit area land has become the primary measure for increasing the total soybean output. However, the determinants that directly affect soybean yield, the regional spatial heterogeneity of yield remain unclear. In this study, data from agricultural statistical yearbooks at both the provincial and prefecture levels in China as well as meteorological data (e.g., temperature, precipitation, and sunshine duration) from 1952 to 2017 (comprising 1952, 1965, 1978, 1990, 2000, 2010, and 2017) were collected, whereupon 13 factors closely related to soybean production were selected from the perspective of planting management measures, natural factors, scientific and technogical levels, social factors, and economic factors. Several boosted regression tree models were built to quantify the relative importance of each factor and to determine the mechanism through which it influenced soybean yield; to analyze the variation characteristics of soybean yield; and to reveal the spatiotemporal characteristics of key driving forces across the national scale and among the four major soybean-producing areas (i.e., the northern spring soybean area, the summer soybean area in the Huang-Huai-Hai Basin, the spring and summer soybean area in the Yangtze River Basin, and the southern soybean area) over a long period since 1952. The following results were obtained. 1) The coefficient of variation of soybean yields in different years ranged from 34.1% to 73.2%, indicating that there were substantial differences in yield across the regions in China. The boosted regression tree model could effectively explain 43.3% of the soybean yield variability and quantitatively revealed the nonlinear relationship between each factor and soybean yield in the national scale. 2) The most important factor affecting soybean yield in China since 1952 was the soybean sown area as a percentage of the total crop sown area (relative importance of 20.9%), followed by the illiteracy rate (18.9%) and fertilizer consumption (pure amount) per hectare (10.7%). 3) Spatial differences existed in the dominant driving factors of soybean yield among different main production areas. The main driving factors of the northern spring soybean area were the total power of agricultural machinery per hectare (13.1%) and the illiteracy rate (11.8%); those for the summer soybean area in the Huang-Huai-Hai Basin were the fertilizer consumption (pure amount) per hectare (25.6%) and pesticide consumption (pure amount) per hectare (18.4%); those for the spring and summer soybean area in the Yangtze River Basin were the R&D expenditure as a percentage of regional GDP (21.5%) and the effective irrigation area as a percentage of the crop sown area (14.3%); and those for the southern soybean area were the fertilizer consumption (pure amount) per hectare and the primary industry as a percentage of regional GDP (13.3%). 4) The soybean sown area as a percentage of the total crop sown area was the most important factor that affected soybean yield during 1952–2017, both before and after the reformation and opening up of China. Additionally, the illiteracy rate and fertilizer consumption (pure amount) per hectare were two other important factors for the period before the reformation and opening up of the country, whereas the total power of agricultural machinery per hectare and annual average temperature were important factors afterwards. This study revealed the determinants of soybean yield and its spatiotemporal heterogeneity in China since 1952 and determined the effective measures for improving the yield of this important crop. These findings should be useful for soybean production-related departments at both the provincial and prefecture levels in China for improving the rational usage of fertilizers and pesticides, increasing the level of mechanization, and enhancing the knowledge level of agricultural producers.