Aiming at the problems that existing energy-saving technologies for new energy vehicles highly rely on high-cost, high-precision sensors such as radars and cameras, and suffer from high implementation costs, this paper studies vehicle operation scene recognition methods for energy-saving driving of new energy vehicles, aiming to develop a low-cost scene recognition model and provide corresponding technical support for energy-saving driving of such vehicles. Firstly, actual road driving data of new energy vehicles with comprehensive scene coverage are collected via real-vehicle tests, with the total test mileage exceeding 126,000 km. Secondly, a data-driven recognition method for typical vehicle operation scenes combining Principal Component Analysis (PCA) and the K-means clustering algorithm is proposed, which classifies three typical operation scenes: congested, urban free-flow, and suburban scenes. Finally, a set of scene-specific driving assistance control strategies is designed, and the potential energy-saving effects of the strategies based on vehicle operation scene recognition are investigated based on a mature powertrain energy consumption simulation model for new energy vehicles. The research shows that under the China Light-Duty Vehicle Test Cycle (CLTC), the energy consumption of new energy vehicles can be reduced by approximately 4.5% through anticipatory perception of vehicle operation scenes and adaptive switching of scene-specific energy-saving control strategies. Moreover, the proposed technology can realize accurate scene recognition without relying on high-cost, high-precision sensors such as radars and cameras, and it can meet the application requirements of low-cost new energy vehicles, including logistics vehicles.