## 1 Introduction  
Cementing is a critical stage in oil and gas well construction and plays a key role in ensuring the well's production lifespan [1]. Improving cementing quality has long been a major focus of research in this field. The ability to predict cementing quality before the operation would enable the early identification of high-risk areas and the implementation of effective measures, ultimately enhancing overall cementing performance. However, cementing quality is influenced by numerous interrelated factors, including formation conditions, wellbore status, fluid properties, and cementing operation parameters [2; 3; 4]. The complex interactions among these factors make it challenging to develop a mechanistic model capable of accurately predicting cementing quality. As a result, there is an urgent need for intelligent models capable of capturing nonlinear interactions among multi-source parameters and enabling accurate prejob cement bond quality forecasting. To address this challenge, scholars have applied various methods to analyze the correlations among key influencing factors. As a result, several preliminary cement bond quality prediction methods have been developed, laying the foundation for further advancements in this field. [PERSON] et al. [5] integrated various logging evaluation methods to develop a comprehensive cement bond quality assessment system tailored to different requirements. [PERSON] et al. [6] explored the feasibility of using real-time acoustic measurements for quantitative cement bond quality evaluation. Meanwhile, [PERSON] et al. [7] applied fuzzy evaluation and gray correlation theory to establish a multi-factor statistical method for assessing cement bond quality. [PERSON] et al. [8] developed a multi-factor prediction model for cement bond quality based on an orthogonal scale wavelet network, significantly enhancing computational speed. [PERSON] [9] applied fuzzy evaluation methods and the analytic hierarchy process (AHP) from fuzzy mathematics to establish a multi-factor prediction approach for cement bond quality, enabling the optimization of cementing parameters. The above methods possess a certain degree of scientific validity; however, they lack adaptability to complex and dynamic conditions, and their scalability and generalization capabilities are limited. In recent years, with the rapid development of artificial intelligence (AI), cement bond quality prediction has witnessed a growing adoption of intelligent methods. A wide range of machine learning models have been explored to enhance prediction accuracy and adaptability under complex downhole conditions. [PERSON] [10] integrated the gray system method with a fuzzy neural network to develop a combined gray-fuzzy neural network approach for cement bond quality prediction, enhancing the model's generalization capability. [PERSON] [11] proposed a cement bond quality prediction method using a least squares support vector machine (LS-SVM), which improved prediction accuracy. [PERSON] et al. [12] optimized the BP neural network model using the LM (Levenberg-Marquardt) optimization algorithm, significantly improving the accuracy of cement bond quality prediction. [PERSON] [13] explored the application of machine learning methods in cement bond quality prediction and introduced the fundamental principles of machine learning models for quality assessment. [PERSON] et al. [14] analyzed the influencing factors throughout the entire cementing process and applied a gray-fuzzy neural network for prediction, providing data support for cementing operations in deep and ultra-deep wells. With the increasing availability of high-resolution data, advanced techniques have been proposed to further improve predictive capabilities. [PERSON] et al. [15] applied gradient boosting regression trees (GBRT) to estimate shear bond strength (SBS) and compressive strength (CS) of fly ash-modified cement, demonstrating superior performance. [PERSON] et al.