Abstract: Management zoning is one of the most important procedures in precision agriculture. Among them, the fuzzy C-means (FCM) and K-means have been commonly used to divide the management zones. However, the conventional methods cannot treat the high-dimensional and non-linear data, due to the data scale and the attribute complexity. This study aims to introduce spectral clustering and deep clustering methods, compare them with FCM and K-means, and evaluate their performance in farmland management zoning, with the goal of providing more scientific decision-making support for precision agriculture. The research object was taken as the farmland at the park scale. According to the field data collection, the soil fertility index (SFI) was constructed to combine the comprehensive weight and multiple soil attributes. The spatial distribution of soil attributes was obtained to quantify the soil fertility level, which was input into the clustering model as a zoning indicator. The Kappa consistency test and analysis of variance were carried out to compare spectral clustering and deep clustering. A systematic evaluation was also made to explore their potential for the management of zoning in precision agriculture. The results showed that the soil fertility index better represented the spatial distribution of the soil fertility in the study area. The fertility level was relatively high in the central area, while the lower was observed in the northeast. The fertility level above the medium level (SFI≥0.5) accounted for 84.06% of the cultivated land, indicating the high soil fertility. The Kappa consistency test showed that there was the highest consistency between the deep clustering and the FCM (the Kappa coefficient was 0.767), followed by that between the spectral clustering and the deep clustering (the Kappa coefficient was 0.657). The consistency between the K-means and the rest was the lowest (the Kappa coefficient was only 0.209-0.234). The significance analysis shows that there were significant differences among the categories that were divided by the four clusterings when the number of clusters was 2 and 3. While there was no significant difference among some categories when the number of clusters was 4. The averaged silhouette coefficient (ASC) and the Calinski Harabasz index (CHI) showed that spectral clustering performed best when the number of clusters was 3. Its zoning was used to significantly distinguish the indicators, such as the soil nutrients, apparent electrical conductivity, vegetation index, and previous crop yield within different management zones. There was a decrease in the coefficient of variation of each variable within each zone. The spectral clustering effectively captured the spatial variability of the soil attributes and crop growth. There was consistency with the spatial distribution of the soil attributes after zoning. In conclusion, the spectral clustering was performed better than before in the spatial data zoning. The map of the management zoning was generated to more accurately reveal the spatial heterogeneity for the farmland management zoning. The finding can provide a more scientific and efficient zoning decision for precision agriculture.