For the strain-temperature cross-sensitivity problem of fiber Bragg grating (FBG) sensor,a temperature compensation algorithm based on Elman neural network with particle swarm optimization (PSO) is proposed.Firstly,based on the principles of fluid mechanics and FBG sensing,a probe-type FBG flow-temperature composite measurement sensor is designed and the flow-temperature composite sensing mechanism is analyzed;then,a flow-temperature composite measurement experimental platform is built,measurement data are obtained,and error analysis is performed;finally,the optimal number of implied layers and the optimal combination of functions are obtained using the PSO-optimized Elman neural network,the flow maximum error and the mean error of the FBG sensor are 0.086 m³h and 0.002 7 m³/h, in the flow range of 2 m³/h—30 m³/h after FBG sensor is compensated,the maximum error and mean square error of temperature are 0.084 ℃ and 0.001 7 ℃,respectively.The experimental results show that the sensor can realize the composite measurement of fluid flow and temperature in the pipeline,and the combination of the PSO-Elman algorithm can effectively reduce the error caused by strain-temperature cross-sensitivity and significantly improve the measurement performance of the sensor.