In order to monitor the concentration and improve the accuracy of harmful gases during layer breeding, the monitoring system based on improved particle swarm optimization back propagation (BP) algorithm was developed. Wireless ZigBee module, sensor module and STM32 module were used to construct the data collection hardware platform at each point of the layer house, the general packet radio service remote communication module was used to transmit the data to the server, the mobile application (APP) software platform was developed to monitor the layer house in real-time. Based on the linearly decreasing weight and the improved learning factor strategy, the particle swarm optimization BP pattern recognition algorithm was used to process the data. Because of the cross-sensitivity caused by common gas sensors in complex environments, the data was not accurate, to improve the accuracy of harmful gas, improved particle swarm optimization optimized BP neural network model was developed. The environmental monitoring data of a chicken house in Baoding, Hebei Province was analyzed, and the effectiveness of the improved particle swarm optimization BP neural network model algorithm was verified by comparing the measured value with the real value of the sensor. The measurement accuracy of the SGP30 carbon dioxide was increased from 81.75% to 94.69%, the measurement accuracy of the MQ135 ammonia was increased from 61.83% to 91.23%, that of the MQ137 ammonia was increased from 70.18% to 91.23%, that of the MQ136 hydrogen sulfide was increased from 62.35% to 92.80%, and that of TGS2602 hydrogen sulfide was increased from 62.97% to 92.80%. The design process of terminal collection node, server and mobile phone APP in layer house environment was given. The functions of the system were verified by experiments.