## 1 Introduction  
As a high-capacity, high-density rail system, the metro significantly alleviates urban surface traffic congestion. However, due to the complexity of the subway pit construction technology, the compactness of the operating site, and the sensitivity of the surrounding environment, there are unforeseen factors and huge safety risks. Due to its low cost and effective mitigation of impacts on adjacent buildings and structures, the open cut method is commonly used in subway pit excavation. However, numerous risk factors and complex construction procedures readily trigger foundation pit instability, causing collapses that result in schedule delays, economic losses, and casualties [1, 2, 3]. Thus, researching early-warning systems for collapse risks is crucial to ensure subway pit construction safety. In the process of subway pit construction, its collapse often has a precursor. For example, excessive deformation leads to cracks in the supporting structure. Therefore, the deformation needs to be controlled within a reasonable range to ensure safe construction. Many scholars have studied the deformation, mainly through empirical methods [4, 5, 6],numerical simulations [7; 8; 9], and field tests [10; 11; 12]. In recent years, artificial intelligence methods have been heavily used in the subsurface space research. Many scholars have used artificial intelligence methods to predict deformation. Researchers [13; 14; 15] used neural network approach to study pit deformation, but it requires a large number of samples and is prone to overfitting. Therefore, some scholars [16; 17] used small-sample machine learning algorithms for single-step prediction of deformation. However, because pit construction is dynamic, single-step prediction does not fully utilize the correlation between before and after monitoring data and does not provide sufficient response time for decision makers. Therefore, this paper intends to select three models suitable for small sample prediction for comparative analysis, which are decision tree regression model (DTR), support vector regression model (SVR), and Gaussian process regression model (GPR). The training set is also extended using a multi-step rolling prediction method with multiple steps to make greater use of the available monitoring data in order to increase the diversity of the training samples. Since it is often difficult for individual monitoring data to reflect the actual situation of pit construction, scholars have proposed a multi-data fusion method to improve the accuracy of early warning. Due to the complex and sensitive environment of subway pit construction, there is a certain amount of fuzzy and random information in the monitoring data that is difficult to judge. Scholars [18; 19; 20] used cloud modeling to quantify the values of evaluation indicators to improve the certainty of information source data. Therefore, the cloud model can be used for the conversion of risk source data in the risk fusion early warning of subway pits. However, due to the different levels of data collection from different information sources, the data obtained are difficult to be harmonized and are prone to bias and even to greater conflict and mutual exclusion. The Dempster-Shafer (D-S) evidence theory can solve the problem of data discrepancy and uncertainty, and it is widely used in the fields of disaster identification and risk early warning [21; 22; 23; 24]. While the traditional D-S evidence theory addresses reliability limitations of single-factor analysis through multi-source fusion [25; 26], its vulnerability to high-evidence conflict causing fusion failure was overlooked. Although later scholars [27; 28; 29] significantly improved conflict resolution accuracy, their single-indicator methods demonstrate compromised interference resistance in diverse scenarios and lack generalizability for representing intense mutual exclusion [30; 31].