## 1 Introduction  
The geological conditions of mountain tunnel engineering are inherently complex, and the information obtained during the investigation phase often fails to comprehensively reflect the actual geological characteristics of the tunnel area. This necessitates the implementation of dynamic design methodologies during construction [1]. Dynamic design imposes stringent requirements on the efficiency and accuracy of surrounding rock classification at tunnel faces during construction phases. Traditional approaches relying on empirical formulas or numerical simulations exhibit limitations in dynamically characterizing the nonlinear deformation features of unexcavated rock masses ahead of tunnel faces [2]. Consequently, algorithm development and deep learning technologies have gained increasing prominence in tunnel and geotechnical engineering research [3; 4; 5; 6; 7]. Convolutional neural networks (CNNs), as prevalent deep learning architectures, have demonstrated extensive applications in image feature classification and recognition [8; 9; 10]. The inherent design of convolutional layers enables CNNs to autonomously extract higher-level abstract features from training datasets. Previous studies have validated the capability of classical models such as AlexNet and ResNet in automatic feature extraction from surrounding rock images [11]. The integration of Principal Component Analysis (PCA) with Probabilistic Neural Networks (PNNs) has shown potential for rock mass stability prediction [12], while Mask R-CNN implementations achieved over 85% accuracy in their mineral content quantification [13]. Although CNN technology effectively replaces manual feature extraction and risk warning tasks, the dynamic identification of rock mass characteristics during construction still faces the following challenges: (1) image acquisition difficulties caused by dust interference, non-uniform illumination, humidity fluctuations, and limited photographic windows [14] and (2) significant heterogeneity in apparent brightness, texture, and grayscale distribution induced by moisture variations and complex fracture patterns, which compromise recognition accuracy [15; 16]. Notably, the longitudinal deformation induced by excavation is correlated with intrinsic rock properties, thereby circumventing the environmental interference inherent to image-based methods. The complete deformation process of rocks surrounding tunnels comprises three distinct phases: pre-excavation for advanced deformation, stabilized deformation following initial support installation, and total deformation prior to secondary lining. Tunnel convergence manifests as a nonlinear process with complex mechanisms, primarily influenced by the surrounding rock stability, excavation-scale effects, and system performance support [17]. Current classification standards for convergence deformation include the [PERSON] [18], [PERSON] [19], and Hoek [20] schemes based on relative deformation, as well as parameter-based systems utilizing absolute deformation, in situ stress, and stress ratios [21; 22]. However, these methods require multiple in situ tests for parameter determination, inevitably disrupting construction operations. Utilizing longitudinal deformation data exclusively could substantially reduce such interference. The Longitudinal Deformation Profile (LDP), as a critical component of the Convergence-Confinement Method (CCM), enables dynamic assessment of support timing and time-dependent self-bearing capacity characteristics, providing practical guidance for construction management [23]. While existing research has extensively analyzed longitudinal deformation data in excavated sections [24; 25], the unexcavated portions remain understudied due to challenges in data acquisition. Therefore, a systematic investigation of LDP characteristics ahead of tunnel faces is imperative for the accurate prediction of surrounding rock properties.