Urban Expansion (UE) is a critical parameter to examine urbanization level. In fact, UE is often accompanied with people, strategies, and urban elements extending over the old edge of city. Hence, UE is a highly complex socioeconomic phenomenon. Dynamic models are very effective to reveal the features and mechanisms of UE. In particular, Cellular Automata (CA) is one of the kinetic models to simulate the natural evolution process and famous for its broad range of applications. A large amount of research findings argue that CA is one of the most potential tools to understand UE. In this study, a multi-factor restricted expansion simulation model of CA (MCES-CA) was proposed according to CA's main features and the main influencing elements of UE. This paper took a middle-zone city named Hengyang City as the case study area. Hengyang City is located in southern Hunan Province of China, and has been positioning to develop into the regional central city of southern Hunan. Hence, it is high significant to reveal the main features and mechanisms of Hengyang City's expansion based on MCES-CA. Firstly, we downloaded LANDSAT images and ASTER DEMs through the internet, and collected Hengyang City's Planning datasets from the Planning Burau of Hengyang Government. Next, we developed the MCES-CA model by the Model Builder Tools of ArcGIS. Then, we ran it to observe the entire expansion process of Hengyang City from 2001 to 2017. We employed the Expanding Intensity Index (EII) and Expanding Velocity Index (EVI) to assess the MCES-CA model. Results show: (1) Hengyang City presented different expansion directions in different ages. (2) The spatial expansion process of Hengyang City was composed of two main stages. (3) The main factors of shaping the expansion process of Hengyang City were geo-environments and development planning. (4) The simulation results were in accordance with the real states of Hengyang's UE. Our findings suggest that MCES-CA is a potentially popular tool to capture the EU processes of cities because it is easy to build and complete. For future research, we can improve the simulation accuracy of MCES-CA by refining transformation rules.