Scene matching requires higher matching speed and memory usage. In order to improve the running speed of the normalized cross correlation algorithm and reduce its memory occupancy rate，this paper focus on researching the steps of fast calculating sub-image's energy. After detailed analysis, the integral graph method has the advantages of flexible and rapid, but the defect is that it needs to spend a lot of memory at the same time, while it is not suitable for the embedded system. Therefore, a fast recurrence method was proposed. In this method, the energy of adjacent pixel values is used to continuously recursive compute. It is not necessary to allocate space for all image energy as the integral image method in the calculation process. Only one row of space can be reserved for the entire energy calculation process in fast recurrence method, which greatly saves the memory usage. The fast recurrence method has the equivalent calculation speed with the integral image method, and the time consuming is only 1/2 of the traditional normalization cross correlation algorithm. In the memory occupancy rate, the fast recurrence method is less than 1/3 of the integral image method, and the larger the size of the real-time graph, the less memory occupied by the fast delivery method. In the normalized cross correlation algorithm, the classical integral graph method and the fast recursive method proposed in this paper are used to calculate the energy of the sub-image's energy, which are both faster than the traditional NCC algorithm. The two algorithms have their advantages. The classical integration image method is fast and flexible, which is suitable for the application scene with high speed requirements, but the memory occupancy rate is not very high. The fast recursive method is fast and saves memory, and is more suitable for the application of embedded systems.