## 4 Discussion and Conclusions  
In this study, we investigated the correlation between glass surface weathering status and the features of color, emblazonry, and type. The results suggested that there is a correlation between the weathering status and the glass type, which may be attributed to different capabilities of different types of glass to resist weathering due to the variations in their in chemical compositions. [PERSON] et al. [34] investigated the influence index for silicate glass weathering and demonstrated that the degree of weathering was determined by the chemical components of the glass. This study confirmed this as significant correlation was only observed between glass type and surface weathering, revealing that the weathering of \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{5}{c}{**Chemical Component**} & \multirow{2}{*}{**Glass Category**} & \multirow{2}{*}{**Weathering Condition**} \\ \cline{2-2} \cline{5-7}  & **PbO** & & & & & & \\ \hline A1 & 0.00 & 0.00 & 0.00 & 78.45 & 7.23 & 1.06 & High-potassium & Non-weathered \\ A2 & 34.30 & 0.00 & 0.00 & 37.75 & 2.33 & 14.27 & Lead–barium & Weathered \\ A3 & 39.58 & 1.36 & 4.69 & 31.95 & 2.93 & 2.68 & Lead–barium & Weathered \\ A4 & 24.28 & 0.79 & 8.31 & 35.47 & 7.07 & 8.45 & Lead–barium & Non-weathered \\ A5 & 12.23 & 0.37 & 2.16 & 64.29 & 12.75 & 0.19 & Lead–barium & Weathered \\ A6 & 0.00 & 1.35 & 0.00 & 93.17 & 1.52 & 0.21 & High-potassium & Weathered \\ A7 & 0.00 & 0.98 & 0.00 & 90.83 & 5.06 & 0.13 & High-potassium & Weathered \\ A8 & 21.24 & 0.23 & 11.34 & 51.12 & 2.12 & 1.46 & Lead–barium & Non-weathered \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Chemical composition and identification results of unknown glass artifacts. Figure 8: Confusion matrices of the performances of the neural network and random forest identification models on the test set. (**a**) Neural network; (**b**) random forest. glass artifacts was chemical weathering. Therefore, suitable protective measures should be employed in the protection and restoration processes to minimize potential environmental threats to ancient glass artifacts [35; 36]. When evaluating the identification performances of the BP neural network and random forest models on the test set, consistent inaccuracies were only observed in the classification of \(\text{LB}_{\text{w}}\) and \(\text{LB}_{\text{nw}}\), and both models presented a superior ability to identify \(\text{HP}_{\text{w}}\) and \(\text{HP}_{\text{nw}}\). The reason for this could be attributed to the sensitive chemical components in lead-barium glass (Figure 4b). Compared to the top five sensitive chemical components for weathering in high-potassium glass, only three chemicals significantly changed (importance index > 0.2) by weathering were reported in lead-barium glass which could bring uncertainty to the weathering identification. Furthermore, this study revealed that the BP neural network model displayed a lower predictive accuracy compared with random forest model, possibly due to the presence of data noise. Data noise refers to inaccuracies introduced by sample selection and measuring area [37]. The random forests could use the randomness of the sample and feature set to efficiently train highly generalized models, which were relatively robust against data noise [38]. The BP neural networks, however, tended to be more vulnerable to outliers and other forms of data noise which may disturb classification and identification outcomes [39]. Through the use of random forest and BP neural network algorithms, our study successfully identified the main and sub classes of eight glass artifacts. Notably, the resulting predictive models demonstrated a high degree of accuracy, contrasting with the methodology proposed by [PERSON] et al. [40], who employed K-means clustering to distinguish glass subcategories based on the elbow rule.