Accurate measurement of the thickness of the cladding oxide film of nuclear fuel assemblies is a key technical challenge to ensure the safe operation of nuclear reactors. In order to solve the problems of low efficiency, insufficient accuracy and temperature sensitivity of traditional detection methods, based on the principle of eddy current testing (ECT), the influence mechanism of temperature drift and oxide film thickness on the detection signal was studied, and a temperature compensation method combined with neural network algorithm was proposed.By constructing a three-dimensional electromagnetic field model, the parameters and detection process of eddy current probe coil were optimized. With the help of JmatPro, the conductivity of zirconium alloy, stainless steel and other materials at different temperatures is simulated, and the influence of temperature on the impedance signal is linear. The developed blade/pen-type probe adopts differential/absolute coil structure respectively, which is suitable for the narrow space inside the nuclear fuel assembly and the peripheral detection needs, combined with the ABB robotic arm to build an automated detection platform, and integrates the LabVIEW-MATLAB hybrid architecture host computer system to achieve real-time data processing. The experimental results show that the systematic measurement error is ≤3μm at room temperature, and the error is controlled within 5μm based on the Elman neural network algorithm under the unsteady temperature condition (25-90°C), which can be used for the quantitative characterization of the thickness of the cladding oxide film.