## 3 Results  
### Subsection Importance Analysis of Influencing Factors  
In this study, we first preprocessed each collected environmental factor, which included the temperature, humidity, illumination, O\({}_{2}\) concentration, NH\({}_{3}\) concentration, PM\({}_{2.5}\) concentration, PM\({}_{10}\) concentration, indoor population, and past CO\({}_{2}\) concentration. The past CO\({}_{2}\) concentration data are used as an example. Figure 8 displays the experimental results of the min-max standardization, which maintain the regularity of the original data and fall within the range of \([0,\,1]\), meeting the requirements for data analysis and prediction. Therefore, in this study, min-max standardization was employed to transform the data for each attribute in the dataset. As shown in Figure 9, for each of the aforementioned environmental factors, the CO\({}_{2}\) concentration fluctuates, which implies that individual environmental factors do not entirely predict changes in CO\({}_{2}\) concentration [42]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Hyperparameter Range** \\ \hline Units & (40, 120) \\ Learning rate & (\(1\times 10^{-6}\), \(1\times 10^{-2}\)) \\ Epochs & (60, 150) \\ Batch size & (50, 160) \\ Dropout rate & (0.0, 0.2) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model’s hyperparameter optimization. To accurately analyze the importance of each environmental factor when determining CO2 concentrations and to filter out the influencing factors, since the past concentration has a significant effect on the current values [43], we calculated the importance of the remaining eight preselected environmental factors with respect to the CO2 concentration via the RF algorithm, and the results are displayed in Table 4. An analysis of Table 4 reveals that, in addition to the past CO2 concentration, the indoor population and illumination are considered the most influential factors for the model when predicting the CO2 concentration in the classroom environment, with relative importance values of 0.84% and 0.54%, respectively. These factors are followed in importance by the PM10 concentration, PM2.5 concentration, temperature, humidity, O2 concentration, and NH3 concentration, with relative importance values of 0.44%, 0.44%, 0.42%, 0.39%, 0.083%, and 0.00059%, respectively. Moreover, the other influencing factors, excluding the NH3 concentration factor, greatly differed from the NH3 concentration factor in their values, so these factors were retained for comprehensive analysis. To further verify the effectiveness of the final selected influencing factors in the LSTM model, we used different numbers of environmental factors as input variables for the model performance evaluation across various indicators. The results are presented in Table 5.  
Figure 8: Preprocessing of past CO2 concentration data. Figure 9: _Cont._  
The results presented in Table 5 indicate that although selecting only the top six features (excluding the NH\({}_{3}\) concentration, O\({}_{2}\) concentration, and humidity) led to a reduction in the model's input dimensions, it also resulted in large errors and suboptimal fitting. When all of the environmental factors were included, the model error decreased, and the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Order of Importance** & **Parameter** & **Relative Importance (\%)** \\ \hline
1 & Indoor population & 0.84 \\
2 & Illumination & 0.54 \\
3 & PM\({}_{2.5}\) concentration & 0.44 \\
4 & PM\({}_{10}\) concentration & 0.44 \\
5 & Temperature & 0.42 \\
6 & Humidity & 0.39 \\
7 & O\({}_{2}\) concentration & 0.083 \\
8 & NH\({}_{3}\) concentration & 0.00059 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The relative importance of each influencing factor.