## 1 Introduction  
Silicone rubber-based flexible ablative materials play a vital role in the field of thermal protection of space vehicles [1]. To optimize their ablative resistance, researchers have carried out numerous investigations on the molecular structure modification of polysiloxanes and the synergistic compounding of ablative-resistant fillers [2; 3; 4; 5; 6]. However, traditional experimental methods based on experience and trial-and-error have faced many challenges, such as long research cycles and substantial resource consumption. These issues seriously hinder the development speed of novel flexible ablative materials, lagging behind application requirements. The data-driven approach has gradually become an important method to accelerate material development. Machine learning (ML), as a typical data-driven technique, excels in data analysis, summarization, and rule mining in the field of material research [7]. It can address the long-cycle and high-cost issues of traditional experimental research, circumventing futile trial and error, and effectively driving the development of new materials [8; 9; 10]. The utilization of machine learning for predicting the performance of silicone rubber-based flexible ablative materials can guide rational material design, which is of great significance for the development of flexible thermal protective materials. At present, machine learning is widely used in the field of polymer materials. One of the most common applications is the utilization of big data to build predictive models of material properties. Predictive models mostly focus on studying properties that do not involve complex chemical changes, such as dielectric properties, glass transition temperature, and mechanical properties, etc. For example, [PERSON] et al. [11] provide a way to apply machine learning models to discover novel electro-photocatalysts and use the models to elucidate electrocatalytic or photocatalytic reaction mechanisms. Another important application is the design and development of new materials. Related research often involves screening new polymer structures by establishing structure-property relationship models. For example, [PERSON] et al. [12] established a prediction model for the relationship between chemical structure and thermal conductivity, and then prepared high-thermal-conductivity materials with new structures based on this model. It is worth noting that traditional machine learning requires a large amount of data to support it. However, few applications related to machine learning are carried out in the field of ablation, with applications mainly in the prediction of ablative performance and optimal design of materials. For example, [PERSON] et al. [13] focused on the oxidation reaction of ultra-high-temperature ceramic materials under thermal flow, and they collected 123 samples and calculated their enthalpy change. Using this information, a mass ablation rate prediction model was successfully constructed. [PERSON] et al. [14] investigated the thermal insulation performance of resin-based coating materials under laser ablation. A model with low prediction errors of thermal insulation performance on the test set was constructed by training a dataset of 190 samples. [PERSON] et al. [15] used ML algorithms to construct models and provided an in-depth analysis of key factors affecting the ablation performance of ultra-high-temperature ceramic matrix composites (UHTCMCs). Eight ML models were successfully used to predict the linear ablation rate (LAR) of UHTCMCs. Previous studies were able to accurately predict the ablative properties by analyzing a large amount of experimental data, reducing experimental time and cost and promoting the development of new materials. Unfortunately, current flexible ablative materials suffer from many problems such as insufficient data accumulation, high cost, and difficulties in material preparation and characterization, as well as difficulty in quickly generating large amounts of data.