<p>Shield operational parameters are inherently noisy and, relative to concurrent geological exploration data, contain considerable redundancy, they must be pre-processed before the datasets input to artificial intelligence models. This paper presents a denoising and compression method for preprocessing shield operational parameters, integrating it with the stratal slicing method for predicting disc cutter wear. The operational parameter signals affecting cutter wear are first denoised using wavelet transform, Fourier transform, rolling average, and autoencoder techniques. The proposed Ring-based Summation Averaging (RSA) and Piecewise Aggregate Averaging (PAA) methods are then used to compress the denoised signals, resulting in compressed sequences composed of key points equal to the number of tunnel rings, effectively matching the geological parameters expanded by the stratal slicing method. Furthermore, the prepared data were tested using the long short-term memory (LSTM) + attention mechanism (AM) model to evaluate its application effectiveness in the Guangzhou Metro Line 18 railway. The results show that data compressed using PAA not only better tracks signal variations but also allows for flexible control of the output length of the compressed sequence. The combination of wavelet transforms denoising (WTD) with PAA exhibited the best wear prediction results, achieving <i>R</i>^<i>2</i> / <i>MSE</i> = 0.95 / 2.21&#xa0;mm. By integrating WTD, PAA, stratal slicing method, and sequence models, a comprehensive and universal methodology is established that can predict disc cutter wear based on initial geological data and shield operational parameters.</p>