## 1 Introduction
Traditional prediction methods such as trend extrapolation models, time series models, and multivariate linear regression models perform well when handling original data and exhibit a clear linear relationship. However, these methods have limitations when addressing nonlinear relationships between indoor CO\({}_{2}\) emissions and their influencing factors. Deep learning has gained popularity in predicting environmental states or data because it can extract complex, high-level hidden information from large high-dimensional datasets. [PERSON] et al. [20] applied the least absolute shrinkage and selection operator (LASSO) regression and whale optimization to optimize the nonlinear parameters, and [PERSON] et al. [21] used dimensionality reduction, clustering, and machine learning algorithms to predict the impact of energy consumption and economic growth on CO\({}_{2}\) emissions. Their study involved clustering the data, reducing the dimensionality via singular value decomposition, and constructing CO\({}_{2}\) prediction models via an adaptive fuzzy inference system and artificial neural networks for each cluster in the self-organizing map. [PERSON] et al. [22] studied the effects of energy consumption, financial development, gross domestic product, population, and renewable energy on CO\({}_{2}\) emissions. They employed long short-term memory (LSTM) [23] to evaluate the impact of these factors on CO\({}_{2}\) emissions. [PERSON] et al. [24] employed a nonlinear autoregressive (NAR) neural network, Gaussian process regression (GPR), and the Holt-Winters seasonal method to forecast CO\({}_{2}\) emissions in the context of combating global warming. [PERSON] et al. [25] conducted a comparative analysis of three deep learning neural network models, namely the artificial neural network (ANN), nonlinear autoregressive network with exogenous inputs (NARX), and LSTM, to determine the most effective model for predicting the temperature, humidity, and CO\({}_{2}\) concentration in greenhouses. However, the model parameters were manually selected, and multiple parameter selection adjustments aimed at further optimizing the model's performance to a greater degree were not present. [PERSON] et al. [26] modified the LSTM structure by removing the forget gate to predict the CO\({}_{2}\) concentration and fine particulate matter (PM\({}_{2,5}\)) concentration, both of which significantly impact the indoor air quality. They proposed the LSTM without the forget gate (LSTM-wF) prediction model, which not only enhanced the prediction performance but also reduced the model complexity in comparison to existing models. Nevertheless, they employed their own selected particles, pollutants, and meteorological parameters as model inputs without conducting a thorough screening of these environmental parameters. Their findings indicated that neural network time series nonlinear autoregressive models outperformed other approaches in terms of predicting future CO\({}_{2}\) concentrations. In summary, the use of a neural network autoregressive model is suitable for predicting CO\({}_{2}\) concentrations, and the LSTM neural network is usually employed to construct models for addressing time series prediction problems. Nevertheless, there is still room for improvement and enhancement in terms of the prediction accuracy. Additionally, there are various factors influencing CO\({}_{2}\) concentrations in classroom environments. These factors can be broadly categorized into environmental design factors and indoor air quality factors, with each major factor comprising several minor factors, which may exhibit certain correlations with one another [27]. Furthermore, the number of people indoors is considered to be strongly correlated with the CO\({}_{2}\) concentration [28]. However, many current studies on CO\({}_{2}\) concentration prediction do not consider the number of people. Therefore, it is essential to compile a comprehensive set of influencing factors that may affect indoor CO\({}_{2}\) concentrations.