<p>In rock tunnel construction, mechanical drilling parameters embed dynamic response information of surrounding rock geological characteristics. High-frequency data collection and analysis facilitate the dynamic identification of the geological conditions at the heading face. However, previous studies mainly centered on inferring the strength of surrounding rock from single-borehole measurement-while-drilling (MWD) data, lacking systematic investigation into the spatial information coupling relationships among multiple boreholes. This study presents a novel method for assessing the integrity of surrounding rock, leveraging the spatial autocorrelation of MWD data. The methodology involves constructing a three-dimensional point cloud model of all boreholes on the heading face, integrating the Principal Component Analysis (PCA) algorithm to fit the principal plane of the heading face, extracting contour features through the Alpha Shapes algorithm, and employing the nearest neighbor interpolation technique and linear interpolation technique to establish a spatial distribution model of rotate pressure. In addition, Moran’s indices are utilized to quantitatively analyze the spatial correlations of parameters, thereby revealing the spatial aggregation patterns of MWD parameters. Results from engineering applications indicate that the evaluation results of this proposed method in practical projects match well with data from Tunnel Seismic Prediction (TSP). Moreover, the Moran’s indices calculated from MWD parameters at varying depths exhibit strong consistency with the evolution trend of surrounding rock integrity. This technology integrates the spatial information of MWD parameters and establishes a comprehensive, full-process evaluation system encompassing “spatial modeling–autocorrelation analysis–integrity quantification,” offering a novel technical solution for the quick assessment of surrounding rock conditions during tunnel construction in complex geological environments.</p>