The work aims to design a high-precision demodulation method for surface acoustic wave (SAW) temperature sensor based on the PSO-ESPRIT algorithm, in order to improve the measurement accuracy of SAW sensor. Based on the ESPRIT spectrum estimation method, the time window length of the Hankel matrix and the K value for calculating the noise variance were used as the input variables of the particle swarm optimization (PSO) algorithm. PSO was used to optimize the parameters of ESPRIT algorithm to improve the frequency estimation accuracy, thus increasing the resolution of frequency estimation of SAW echo signal and realizing high-precision demodulation of SAW temperature sensor. Simulation and experimental results indicated that compared with other spectrum estimation algorithms, the designed method had the smallest frequency error in the estimation of SAW echo signals, and the standard deviation was less than 0.66 kHz. When the designed algorithm was used in the temperature demodulation of the SAW temperature sensor, the error between the obtained temperature value and the actual temperature was less than 0.4 °C. According to the test results, the designed temperature demodulation method improves the frequency demodulation accuracy of the SAW echo signal, and can be used for the demodulation of the SAW temperature sensor to realize the real-time monitoring of the temperature during the storage and transportation of food packaging.