Neural networks face challenges when processing heterogeneous columns of tabular data. This study proposes a method to transform table features into low-frequency representations to enhance the neural network's parsing capability. Experiments show that this approach significantly improves network performance and computational efficiency. The research provides new insights and methods for neural networks to handle complex tabular data.