## 1 Introduction
With the rapid development of artificial intelligence technology in recent years, techniques such as machine learning have achieved certain results in obtaining laws between TBM tunneling parameters and surrounding rock information due to their powerful ability to deal with nonlinear problems. For example, [PERSON] et al. [25] demonstrated nonlinear relationships between TBM tunneling parameters and surrounding rock parameters based on deep neural networks, effectively identifying rock mass conditions. [PERSON] et al. [26] used a support vector classifier as the prediction modeler of geological conditions and achieved satisfactory prediction accuracy. [PERSON] et al. [27] and [PERSON] et al. [28] used the semi-supervised learning method and the long short-term memory network based on the global attention mechanism to observe surrounding rock types, respectively, achieving good performance. Thus, machine learning algorithms can be used to identify SRE. This can enable more accurate identification of SRE and provides more accurate guidance and evaluation for TBM construction. This paper aims to propose a set of universal excavatability classification systems for surrounding rocks. In order to increase the practicality of this system, machine learning algorithms were used to achieve accurate and continuous intelligent identification of the -SRE classification. Therefore, this paper comprehensively analyzed the correlations between TBM tunneling parameters and geological information using TBM field data and the technique for order preference by similarity to an ideal solution (TOPSIS) method and established an SRE classification system based on TBM rock-breaking performances. This system is not influenced by a single evaluation index and has high practicality. The classification system was also verified by engineering cases. The SRE perception model based on deep forest (DF) was constructed using grid search for hyperparameter optimization, incorporating prior knowledge. This model was used to identify SRE in order to reduce the limitations of the proposed system and intelligently perceive SRE grades. Then, this model was compared with four well-developed machine learning models, i.e., decision tree, random forest, support vector classifier, and deep neural network. In addition, the contributions of input features were measured using the random forest (RF) method to explore their influences on the model. The findings can provide more accurate and rapid access to the characteristics of surrounding rocks at the tunnel face of TBM tunneling when engineers cannot observe the surrounding rock in real time. It is important for avoiding construction risks, controlling construction costs, and improving TBM tunneling efficiency and intelligent construction. This paper is organized as follows: following the Introduction, Section 2 briefly describes the project's overview, data sources, and data processing and research methods; Section 3 proposes and validates the SRE classification process based on the rock-breaking performance of TBMs and analyzes the feasibility of DF as an intelligent identification method; Section 4 presents the limitations and discussion; Section 5 provides the concluding remarks.