The ability to perform complex reasoning on semantic data streams generated by sensors has recently become an important research area in the Semantic Web community. Currently, most RDF stream processing systems are implemented based on SPARQL (W3C Standard Protocol and RDF Query Language), but these engines have limitations in capturing complex user requirements and processing complex reasoning tasks. In response to this problem, this study combines and extends Answer Set Programming (ASP) technology for continuous processing of RDF streams. In order to verify the effectiveness of this method, we firstly take the smart home ontology as the experimental object, and analyze the common characteristics and complex events between the sensor devices to build the ontology library; then generate instance objects based on the ontology library and generate RDF data stream through middleware. Next, through extending ASP, making full use of its expression, and reasoning capabilities and reducing the reasoning time, a window partitioning strategy for the RDF stream in this method is designed. The static knowledge base is selectively loaded according to the user’s request. Finally, the comparison with Sparkwave and Laser through experiments proves the performance advantage of this method in terms of latency and memory.