In order to solve the problem of temperature drift of eddy current sensors, a temperature compensation correction model based on genetic optimization wavelet neural network(GA-WNN) algorithm for eddy current sensor is persented. The GA-WNN neural network model was established by calibrating the eddy current sensor and monitoring its working temperature with LM35 temperature sensor. The model uses genetic algorithm to optimize the weight and threshold of the wavelet neural network, which improves the slow training speed of the wavelet neural network and overcomes the defect that is easy to fall into local optimum. The results show that the sensitivity temperature coefficient after compensation is raised from 8. 69×10^-3/℃ to 3. 48 × 10^-4/℃and the zero temperature coefficients is raised from 4. 78 ×10^-3/℃ to 1. 85 × 10^-4/℃. Both have increased by one order of magnitude, and successfully achieved temperature compensation.