Aiming at the problem of temperature drift of Hall effect force sensor, a new temperature compensation model of chaotic adaptive whale optimized BP neural network (CIWOA-BP) was proposed. This model uses Cubic mapping as the initial whale population generation method to improve the quality and distribution uniformity of the population. The adaptive weight was introduced to adjust the shrinking and bounding mechanism of the whale to improve the global search ability and convergence of the algorithm. The CIWOA algorithm is used to optimize the initial weights and thresholds of the back propagation (BP) neural network, so that the model has better measurement accuracy and stability. Research results indicate that after temperature compensation, the temperature coefficient of sensitivity for the Hall effect force sensor decreases from 5.08×10-3/℃ to 9.8×10-5/℃, reducing by two order of magnitude. The temperature-induced relative error decreases from 19.82% before compensation to 0.38%, which is reduced by over 52 times, effectively mitigating the influence of temperature on measurement results.