The contact force feedback at the end of the surgical probe is one of the important factors to ensure the safety of surgery. In this article, a 3D end-force measurement method based on the kernel extreme learning machine (KELM) neural network is studied to meet the need of 3D end-force measurement of a puncture surgical probe based on optical fiber sensing. First, a surgical probe structure with implantable optic fiber sensors is designed, and four fiber Bragg grating (FBG) sensors are implanted into the probe, three of which are used for force measurement and the rest is used for temperature compensation. Then, by analyzing the relationship between probe stress and strain, a 3D end-force sensing model of the probe based on FBG is formulated. To eliminate the cross-effect of temperature change on the optical fiber sensor, a temperature compensation method for optical fiber sensing is studied. Finally, to evaluate the effectiveness of the neural networks, the temperature of the probe implanted in FBG is calibrated. The force measurement effect is verified in the normal temperature and temperature changing condition. The results indicate that the KELM neural network has better results in measuring the 3D end-force of the probe, and average measurement errors of KELM network in X, Y, and Z directions at room temperature are 0. 22% , 0. 99% , and 0. 65% , respectively. Under temperature changing condition from 20℃ ~ 40℃ , the average measurement errors in the X, Y, and Z directions are 1. 32% , 1. 03% , and 2% , respectively. The 3D end-force measurement method of the KELM neural network studied in this article has small measurement error, which has broad application prospects in the field of force feedback of surgical robots.