In view of the problems such as poor adaptability to complex working conditions, insufficient nonlinear error compensation accuracy and low level of instrument intelligence in high oxygen and trace oxygen measurement in the field of gas detection, this paper designs a gas detection of BP neural network (MAPSO-BP) optimized by lightweight modified adaptive particle swarm algorithm. Instrument, the system builds a multi-sensor embedded platform to realize synchronous acquisition and fusion compensation of multi-parameters including temperature, pressure, flow and concentration, uses a microcontroller unit to run MAPSO-BP network in real time for nonlinear error correction, and develops an embedded human-computer interaction system based on Qt, supporting network communication, data storage, real-time alarm and cloud data synchronization functions enhance the intelligence level of the instrument. The system prototype designed in this paper is tested for system stability, anti-interference ability test and comparative experiments with the existing error compensation model. The results show that the error compensation method proposed in this paper and the designed system prototype are compared with the current mainstream error compensation method. The absolute error average of high oxygen and micro-oxygen measurements are respectively reduce by 20% and 25%; effectively solve the problem of low measurement accuracy of sensors under complex working conditions, and provide a feasible solution for the precision and low cost of gas sensors.