The classical functional linear regression model (FLM) and its extensions, which are based on the assumption that all individuals are mutually independent, have been well studied and are used by many researchers. This independence assumption is sometimes violated in practice, especially when data with a network structure are collected in scientific disciplines including marketing, sociology and spatial economics. However, relatively few studies have examined the applications of FLM to data with network structures. We propose a novel spatial functional linear model (SFLM), that incorporates a spatial autoregressive parameter and a spatial weight matrix into FLM to accommodate spatial dependencies among individuals. The proposed model is relatively flexible as it takes advantage of FLM in handling high-dimensional covariates and spatial autoregressive (SAR) model in capturing network dependencies. We develop an estimation method based on functional principal component analysis (FPCA) and maximum likelihood estimation. Simulation studies show that our method performs as well as the FPCA-based method used with FLM when no network structure is present, and outperforms the latter when network structure is present. A real weather data is also employed to demonstrate the utility of the SFLM.