为解决基于图像的掌子面前方围岩预测易受环境干扰的问题，提出一种以纵向位移曲线（LDP）（可反映围岩自身的强度属性）为输入的轻量化预测思路。首先通过数值模拟对3类围岩（GSI=15、30、50）在9种组合形式下的LDP曲线演化规律进行分析及总结，再基于融合时空注意力机制的一维卷积神经网络(1D-CNN)构建掌子面前方围岩预测模型。研究结果表明： 1) 均匀地层方案总体上变形量先增大再减小； GSI为15时，整体变形量较大； 含地层分界面的方案在地层分界面之前变形量先增大再减小，在地层分界面之后的变形量与地层分界面之后的岩性参数相关。2) 融合时空注意力机制的一维卷积神经网络模型以LDP纵向变形曲线数据进行输入，根据LDP纵向变形曲线的特征对地层进行分类，其预测准确率为0.88。3) 将所提出的模型与不同变体模型进行对比分析，结果显示融合时空注意力机制的1D-CNN在参数量较低的情况下取得了最高的准确率。

Image-based prediction of rock mass ahead of the tunnel face is vulnerable to environmental disturbances, whereas the longitudinal displacement profile (LDP) reflects the intrinsic strength attributes of the rock mass. The authors propose a lightweight prediction approach using LDP as the model input. Numerical simulations are conducted to analyze and summarize LDP evolution patterns for three rock mass qualities (geological strength indices (GSIs) of 15, 30, and 50) under nine stratigraphic combinations. Based on these results, a classification mode for rock mass ahead of the tunnel face is developed using a one-dimensional convolutional neural network (1D-CNN) enhanced with spatiotemporal attention, with LDP sequences as inputs. The main findings are as follows: (1) For homogeneous strata, deformation generally increases and subsequently decreases as excavation advances; at a GSI of 15, the overall deformation is larger. For strata containing an interface, deformation increases and subsequently decreases before the interface, with postinterface deformation dependent on lithological parameters. (2) The proposed 1D-CNN with spatiotemporal attention achieves a test accuracy of 0.88 for strata classification from LDP sequences. (3) Comparative experiments with variant models show that the 1D-CNN with spatiotemporal attention attains the highest accuracy with a relatively low parameter count. By combining LDP evolution characteristics with deep learning, this lightweight method offers a new approach for predicting rock mass class ahead of the tunnel face.