The optical fiber pressure sensor needs temperature compensation because its performance is greatly affected by temperature. To solve this problem, a software compensation scheme combining grey wolf optimization and least squares support vector machine (GWO-LSSVM) algorithm is proposed. The penalty factor ζ and kernel parameter σ of least squares support vector machine are iteratively optimized by grey wolf optimization algorithm within the specified range to construct the compensation algorithm model. In different temperature situations, the input and output data of the sensor are measured by calibration test and are divided into test set and training set. By taking the root mean square error which is calculated from the predicted values of the test set as the fitness function, the temperature compensation problem is transformed into a convex quadratic optimization problem with constraints. The results show that compared with previous compensation, the sensitivity temperature coefficient of the fiber optic pressure sensor after temperature compensation is increased from 9. 405 × 10 -3 / ℃ to 1. 201 6 × 10 -4 / ℃ , and the relative value of the additional temperature error is increased from 28. 215% to 0. 481%. The temperature stability of the sensor is greatly improved.