With continuous socioeconomic development, the land ecological security pattern is becoming increasingly severe. In this article, an evaluation system for land ecological security patterns is constructed based on existing research. Python’s TensorFlow framework is used to construct a support vector machine (SVM) based on the land ecological security evaluation method. Finally, based on a small sample scenario, the land ecological security pattern of the urban agglomeration in Zhejiang Province is evaluated and analyzed, and optimization strategies are proposed. The results indicated (1) from 2015 to 2021, the land ecological security levels of most cities in Zhejiang Province gradually decreased and were generally low, with most cities moving from relatively safe levels to critical security levels; (2) significant spatial changes occurred in the overall land ecological pattern of the Zhejiang urban agglomeration, which changed from “higher in the north and south, lower in the middle” to “lower in the southwest, slightly higher in other areas, and extremely low in a few areas (Huzhou city, Zhoushan city)”; and (3) considering social indicators, the land ecological security pattern in the urban ecosystem of Zhejiang Province primarily deteriorated because the per capita cultivated area and the proportion of cultivated land in the natural indicators significantly decreased, and the per capita residential and urban construction land areas significantly increased.