Existing research on human behavior based on the PIR (Passive InfraRed) sensor data is limited by the spatial-temporal distribution of motion, clustering, and so on. The reconstruction of behavior trajectory and analysis of semantic features are relatively few, so it is urgent to develop new modeling and behavior analysis methods for the PIR data. This paper attempts to reconstruct the spatial and temporal trajectories by using the PIR (Passive InfraRed) sensor monitoring data. PIR sensors have the characteristics of low price and privacy protection. However, because only Boolean logical response sequence can be obtained by PIR sensors, it is difficult to accurately obtain movement trajectories. Its application has been relatively limited, and it is difficult to conduct movement behavior feature analysis. Traditional PIR sensor network analysis methods are mostly based on the signal extraction idea, which cannot integrate geometric features and semantic information at the same time. By introducing the geometric algebra theory, the sensor scene network can be constructed to realize the path expression and calculation of dynamic network in the geometric algebra space. This paper analyzed the characteristics of human movement features and semantic features, established semantic units, and realized the transformation from spatial data to semantic features. The spatial and topological characteristics of individual and crowd movements were analyzed. We proposed a generation and transformation-based methods of algebraic structures in the geometric algebra system, which provides a new idea and mathematical basis for solving non-deterministic problems such as the PIR sensor network data based analysis, and can provide reference for the construction of internet of things GIS.