## 1 Introduction  
Highways are extremely important infrastructure in most countries, ensuring connectivity and development between different regions, especially in mountainous and hilly areas. However, due to the long construction period of highway tunnels and the large disturbance to surrounding rock, there are unpredictable factors and huge safety risks. In the construction process, the drilling and blasting method is widely used in tunnel excavation due to its low cost and strong geological applicability. However, due to many risk factors and complex construction procedures, it is easy to cause collapse accidents [1]. Once the tunnel collapse occurs, it will cause delays in construction, economic losses, and even casualties. Therefore, in order to ensure safe construction during highway tunnel engineering, it is of great significance to accurately evaluate the collapse risk of tunnel construction. Aiming at the problem of tunnel collapse risk assessment, the main research methods include single information source risk assessment methods such as analytic hierarchy process [2; 3; 4], fuzzy comprehensive evaluation method [5], risk matrix method [6; 7], network analysis method [8], Bayesian network [9; 10; 11] and fault tree method [12; 13], as well as multi-source information fusion assessment methods such as neural network [14; 15], rough set [16], D-S evidence theory [17] and maximum entropy method [18]. Among them, the results of using a single information source to analyze the risk of tunnel collapse are deviate slightly from the actual situation. This is because the single-source information cannot reflect the actual construction situation. The evaluation results are inaccurate and cannot provide accurate suggestions for decision makers. The fusion model can better understand the risk factors and greatly improve the accuracy of the prediction results. It is one of the most effective means to study the risk assessment of tunnel collapse. A multi-classifier information fusion model is proposed [19]. Support Vector Machine and D-S evidence theory are used in the model to evaluate the health risk of subway structure under uncertain conditions. It is proved that the fusion model has better robustness and accuracy than the single-classifier model. The safety risk of adjacent buildings during tunnel excavation is perceived through the cloud model and the improved D-S evidence theory. The reliability of the safety risk perception results was tested when the measurement factors were at different deviation levels [20]. The improved multi-source information fusion method is used to evaluate the risk of subsea tunnel excavation models. The results show that the prediction results of the improved algorithm are in good agreement with the actual water intrusion phenomenon observed in the model test. Based on the previous research results, the size of the evidence conflict coefficient determines the degree of agreement between the evidence fusion results and the actual results [21]. [PERSON] considered the on-site inspection of experts and the monitoring of on-site instruments. At the same time, a weighted average was used for non-conflict evidence fusion, and traditional D-S fusion was used for high-conflict evidence. However, the advanced geological forecast data are not considered. Due to the complex geological conditions of mountain tunnels, the advanced geological forecast data can directly describe the geological conditions before excavation, which has an advanced expected effect on the construction and can reflect the collapse risk to a certain extent. At the same time, the traditional D-S fusion is used for high conflict evidence, while the traditional D-S evidence theory is very insensitive to high conflict evidence, which leads to inconsistency between the fusion result and the actual result [22].