## 5 Conclusions  
In this study, the slime mould algorithm is incorporated into the support vector machine model to improve the accuracy of the model and the efficiency of the system. The parameters of the support vector machine are optimally selected by the slime mould algorithm without changing the structure of the original algorithm. When training the model, indicators such as training set accuracy, test set accuracy, precision, recall, and F1 score are used to evaluate the model's merit.  
The experiments show that the model proposed in this paper performs well on the dataset and has a substantial improvement over all other classification models. In addition, the analysis of the running time and the number of iterations can be obtained that the SMA algorithm is fast and time-consuming in finding parameters, which helps to improve the system efficiency and save the running cost. In summary, this study concluded that the support vector machine model incorporating the slime mould algorithm strategy is a better choice for the classification of glass artefacts and gives more accurate classification results than before. The algorithm proposed in this study will be useful in revealing the potential trade relations and cultural exchanges between different regions in ancient times. Future researchers can adopt the algorithm proposed in this paper to classify ancient glass, which will be helpful in deeply exploring and inferring a more accurate and complete ancient trade network and cultural exchange pattern.  
The dataset used in this study is based on the proportions of the chemical composition obtained from sampling points on the surface of the artefacts. It is clear that the main chemical composition of ancient glass artefacts from around the world is similar [49, 50]. Therefore, the results obtained within the scope of this study can also be applied to glass artefacts excavated around the world for classification purposes. However, when applied to glass objects with significantly different compositional ratios, the results should be validated using a different methodology.  
Future research directions for this study can be summarised as follows:  
1. The model will determine the classification of the newly excavated glass;
2. The model can be compared with deep learning methods. It is limited by the small amount of data, and deep learning methods have not been utilised in this study;
3. In conjunction with the location of the research samples and the distribution patterns of the material content of the local soils, the trade relationships between the ancient regions are determined.  
**Author Contributions:** Conceptualisation, Y.G.; data curation, Y.G.; writing--original draft preparation, Y.G.; writing--review and editing, W.Z. and W.L.; supervision, W.Z.; project administration, W.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 62276032.  
**Institutional Review Board Statement:** This study did not require ethical approval.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are provided in the 2022 China Undergraduate Mathematical Contest in Modeling. The authors do not have permission to share the data.  
**Conflicts of Interest:** The authors declare no conflict of interest.