With the continuous development of sensing technology, more and more sensor-based wireless sensor detection systems have emerged. These sensor systems require data analysis of the collected data. Therefore, the data analysis of sensors plays a vital role in the accurate detection of wireless sensing systems. Firstly, the design SAW micro-pressure sensor is measured, and the mathematical model is established by the least square method,and the relationship between the measured frequency change and the corresponding load force is analyzed by data fitting. Subsequently, a BP neural network model was constructed to train the collected data and predict the relationship between the input and output of the SAW pressure sensor. Finally, genetic algorithm is used to optimize the BP neural network. The results show that after optimization, the error of BP neural network is reduced by nearly 45% compared with that before optimization, thus verifying the feasibility of genetic algorithm to optimize BP neural network. This method provides an important research basis for the development of wireless sensor detection system for acoustic surface wave micro-pressure sensor.