## 5 Conclusions and Future Work  
In this study, the evaluation index system of the customer satisfaction scoring of China Mobile was established through the combination of systematic clustering and exploratory factor analysis; after 28 core indicators were screened out by systematic clustering to avoid redundancy of information, 8 core factors were identified by exploratory factor analysis; on this basis, the GBDT algorithm was applied to predict the customer satisfaction scoring. The conclusions are as follows:  
1. In this paper, we obtain eight core influence factors through the double dimensionality reduction combining systematic clustering and exploratory factor analysis, which is more reasonable and can obtain the most core influence factors compared with [PERSON] [22], who selects the core influence factors of mobile users of Wuhan communication through correlation. Core factor 1 has the highest weight of 24.952%, i.e., the status quo of indoor voice problems has the greatest influence. Factor 1 has seven core influencing factors. Therefore, if mobile operators want to improve user satisfaction, they need to improve these seven core impact factors, including whether they have encountered network problems, residential area, underground, no signal of mobile phone, sudden interruption during the call, inaudible intermittent and intermittent call with noise, and one party cannot be heard during the call. 2. General machine learning prediction algorithms have an accuracy of about 70% [23; 40; 41], and the prediction accuracy of this study can reach 99.96%, which is a very high accuracy when predicting. Highly accurate satisfaction prediction can help operators more accurately adjust their operational strategies, so as to improve their market competitiveness. In addition, improved user satisfaction can help promote the development of the communication industry and promote national informatization and economic growth. 3. Although this study is based on data from Chinese operators, it can be generalized to a certain extent to foreign operators or other related satisfaction rating studies. However, it should be noted that factors such as culture, social background, the level of economic development, laws and regulations in different countries and regions will have an impact on the user satisfaction evaluation system. Therefore, it is necessary to make corresponding adjustments when applying the research results to other countries or regions. If one would like to apply them for Amazon's mobile marketplace in the UK, the following areas can be explored further: 1. Cultural factors are crucial to user satisfaction. The UK and China have different cultural backgrounds that influence values, socialization and communication habits. Amazon, as a multinational company, needs to adapt to the cultural expectations of UK users. UK users may value privacy more and have \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **MSE** & **RMSE** & **MAE** & **MAPE** & **R\({}^{2}\)** \\ \hline reasonable & 0.014 & 0.118 & 0.082 & 1.253 & 0.997 \\ dataset & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of model prediction assessment. different attitudes toward data use and sharing. Therefore, it is important to understand the cultural characteristics of UK users to accurately reflect their needs and expectations when evaluating user satisfaction. * Economic factors are also important. The economic level, spending power and shopping habits in the UK are different from those in China, which will affect user demand for mobile services and satisfaction levels. Amazon's pricing strategy and package selection in the UK market must take into account the purchasing power and preferences of UK users. Therefore, economic factors need to be thoroughly analyzed in the rating system to more accurately reflect user evaluation and satisfaction. * Regulatory differences also need to be taken into account. The UK and China have different privacy and user rights regulations.