In the process of metal processing, heat treatment is a common metal processing method, which is usually used to change the mechanical properties of metal alloys to control such as hardness, strength, toughness, ductility, and elasticity. The high-frequency heat treatment is to rapidly heat the surface of the steel with high frequency, stop the heating when it reaches the quenching temperature, and use an appropriate coolant to implement quenching. In the process of processing, it is often necessary to set processing parameters for the processed products, so as to ensure that each processed product meets the requirements. This manual setting method is not only inefficient but also needs to rely on the experience of field personnel to set parameters. Even so, the problem of artificially setting wrong parameters still occurs. Therefore, this paper applies machine learning technology to analyze the correlation between processing parameters and establishes a predictive evaluation model in combination with hyperparameter optimization, and then predicts the best processing parameters. In order to achieve the concept of intelligent production line, and can reduce the problems caused by human setting errors, thereby improving efficiency.