Taking the watershed as the scale to conduct landscape ecological risk assessment and landscape pattern optimization is conducive to providing a scientific basis for the improvement of watershed ecosystem services and the management of human activities. The ecological risks faced by the Fujiang River Basin are affected by multiple factors, and the optimization of landscape pattern is an effective method to deal with ecological risks. In this research, 10 factors from three aspects (e.g. natural, society, and landscape pattern) were selected to establish the index system. The Spatial principal component analysis (SPCA) was used to evaluate the ecological risk of watershed landscape, and then based on the results of ecological risk assessment and ecological sources, the minimum cumulative resistance model (MCR) and network analysis were used to optimize the landscape pattern of the watershed. The results show that ①The spatial principal component analysis method can effectively evaluate the spatial distribution of landscape ecological risks. The level of ecological risk in the northwest was higher than in the southeast, which was mainly affected by the two dimensions of natural and landscape pattern factors. ②The ecological risks problem faced by the Fujiang River basin are severe. The area of ecological risk grads of moderate and above is 25596.51 km², accounting for 65.35% of the total area of the study area. ③The ecological sources are mainly forests and water areas, with a total area of 11194.28km², accounting for 25.58% of the total area basin of the basin. ④The network ecological structure is composed of 15 ecological sources, 53 ecological nodes, 41 auxiliary ecological corridors with a total length of 5229.04 km and 1 main "central axis" corridor, which can effectively reduce the ecological risk in the study area and promote the flow of material and energy in the study area. By comparing the connectivity of the landscape pattern before and after optimization in the study area, it can be found that the connectivity after optimization has been significantly increased. The research results are helpful to improve the ecological stability of the study area, and provide a scientific basis for the landscape ecological risk assessment and landscape pattern optimization research.