This paper mainly studies the effect of artificial lighting environmental factors on the psychological emotions of observers in the large and practical space of the museums. The purpose is to reveal the relationship between the observers’ response and the artificial lighting condition in the actual art museum space. Field research regarding three art museums in Japan was carried out and the optical environment parameters applied in those museums were quantified. The innovation method is to define the artificial lighting environment space in the way of classified lighting design. Thirty one observers were invited to evaluate the three art museum’s lighting environment. In addition, this paper analyzes and discusses the influence of the actual spatial lighting parameters of museum buildings on observers’ psychological emotions (comfort, clarity, preference and warmth), under three modes of illuminance and correlated colour temperature (CCT) combination. Using one-way analysis of variance and correlation analysis, through analysis get the correlation of the four evaluation and three lighting environments indexes are less than 0.05, the observer in an environment with high illuminance and a high CCT had higher psychological evaluation of the art museum.

In the art museum, lighting plays a crucial role in visitors’ understanding and appreciation of exhibits. This paper mainly explores the influence of different lighting mode (general lighting, accent lighting, and mixed lighting) and correlated color temperature (CCTs) (4000 K and 2700 K) changes on the observers’ psychology to find the best lighting environment. Based on the simulation carried out at the National Museum of Western Art in Japan, we made six diverse lighting designs. 31 observers were invited to evaluate each scheme, and the results of subjective evaluation were analyzed by means of one-way analysis of variance and the paired sample T-test. According to the results of data analysis, suggestions can be given for art gallery lighting design. It is not recommended to use general lighting alone in art museums, and it is better to use mixed lighting. When the CCT is 4000 K, only accent lighting can be used. When the CCT is 2700 K, the mixed lighting effect is better.

This paper investigated foreign language learning efficiency in four different illumination environments (in different illuminance and color temperatures), focusing on the influence of the illumination environment on foreign language learners’ sentimental status, by means of foreign language skills testing in mind-map, objective evaluation of physiological reaction, and subjective evaluation of psychological reaction. It was shown that in different illumination environments, the language skills of foreign language learners were different, and their psychological and physiological reactions varied, which influenced the efficiency of foreign language learning. The results indicated that the ideal learning space was in high illuminance and low color temperature, which increased the stimulation in foreign language learners; promoted the formation of optimistic sentiment; and enhanced their interest in, and the quality and efficiency of, foreign language learning.

This study investigates how lighting conditions influence emotional and neural responses in a standardized, simulated museum environment. A multimodal evaluation framework combining subjective and objective measures was used. Thirty-two international students assessed their viewing experiences using 14 semantic differential descriptors, while real-time EEG signals were recorded via the EMOTIV EPOC X device. Spectral energy analyses of the α, β, and θ frequency bands were conducted, and a θα energy ratio combined with γ coefficients was used to model attention and comfort levels. The results indicated that high illuminance (300 lx) and high correlated color temperature (4000 K) significantly enhanced both attention and comfort. Art majors showed higher attention levels than engineering majors during short-term viewing. Among four regression models, the backpropagation (BP) neural network achieved the highest predictive accuracy (R² = 88.65%). These findings provide empirical support for designing culturally inclusive museum lighting and offer neuroscience-informed strategies for promoting the global dissemination of traditional Chinese culture, further supported by retrospective interview insights.

With the rapid development of urbanization, ambient air pollution is becoming increasingly serious. Out of many pollutants, fine particulate matter (PM_2.5) is the pollutant that affects the urban atmospheric environment to the greatest extent. Fine particulate matter (PM_2.5) concentration prediction is of great significance to human health and environmental protection. This paper proposes a CNN-SSA-DBiLSTM-attention deep learning fusion model. This paper took the meteorological observation data and pollutant data from eight stations in Bijie from 1 January 2015 to 31 December 2022 as the sample data for training and testing. For the obtained data, the missing values and the data obtained from the correlation analysis performed were first processed. Secondly, a convolutional neural network (CNN) was used for the feature selection. DBILSTM was then used to establish a network model for the relationship between the input and actual output sequences, and an attention mechanism was added to enhance the impact of the relevant information. The number of units in the DBILSTM and the epoch of the whole network were optimized using the sparrow search algorithm (SSA), and the predicted value was the output after optimization. This paper predicts the concentration of PM_2.5 in different time spans and seasons, and makes a comparison with the CNN-DBILSTM, BILSTM, and LSTM models. The results show that the CNN-SSA-DBiLSTM-attention model had the best prediction effect, and its accuracy improved with the increasing prediction time span. The coefficient of determination (R²) is stable at about 0.95. The results revealed that the proposed CNN-SSA-DBiLSTM-attention ensemble framework is a reliable and accurate method, and verifies the research results of this paper in regard to the prediction of PM_2.5 concentration. This research has important implications for human health and environmental protection. The proposed method could inspire researchers to develop even more effective methods for atmospheric environment pollution modeling.

Assessing the visual quality of rural landscapes is pivotal for quantifying ecological services and preserving cultural heritage; however, conventional ecological indicators neglect emotional and cognitive dimensions. To address this gap, the present study proposes a novel visual quality assessment method for rural landscapes that integrates multimodal sentiment classification models to strengthen sustainability metrics. Four landscape types were selected from three representative villages in Dalian City, China, and the physiological signals (EEG, EOG) and subjective evaluations (Beauty Assessment and SAM Scales) of students and teachers were recorded. Binary, ternary, and five-category emotion classification models were then developed. Results indicate that the binary and ternary models achieve superior accuracy in emotional valence and arousal, whereas the five-category model performs least effectively. Furthermore, an ensemble learning approach outperforms individual classifiers in both binary and ternary tasks, yielding a 16.54% increase in mean accuracy. Integrating subjective and objective data further enhances ternary classification accuracy by 7.7% compared to existing studies, confirming the value of multi-source features. These findings demonstrate that a multi-source sentiment computing framework can serve as a robust quantitative tool for evaluating emotional quality in rural landscapes and promoting their sustainable development.