Spatial filtering is a critical component in the realm of signal processing, widely utilized in various technological and scientific fields. This paper proposes a novel method for spatial filtering by integrating advanced signal processing techniques with deep learning models. We begin by examining the fundamental theories of spatial filtering, emphasizing the enhancement of signal reception and interference reduction. The paper then progresses to the implementation of deep learning algorithms, which are tailored for real-time environment adaptation and superior signal discrimination. Our method diverges from traditional spatial filtering techniques by combining sophisticated signal processing with deep learning, leading to a marked improvement in filter performance and a notable increase in adaptability to diverse environmental conditions. Comparative analyses with conventional spatial filtering methods are provided to showcase the advantages of our integrated approach in challenging signal environments.