To solve the increasingly serious traffic congestion and reduce traffic pressure, the bidirectional long and short-term memory (BiLSTM) algorithm is adopted to the traffic flow prediction. Firstly, a BiLSTM-based urban road short-term traffic state algorithm network is established based on the collected road traffic flow data, and then the internal memory unit structure of the network is optimized. After training and optimization, it becomes a high-quality prediction model. Then, the experimental simulation verification and prediction performance evaluation are performed. Finally, the data predicted by the BiLSTM algorithm model are compared with the actual data and the data predicted by the long short-term memory (LSTM) algorithm model. Simulation comparison shows that the prediction results of LSTM and BiLSTM are consistent with the actual traffic flow trend, but the data of LSTM deviate greatly from the real situation, and the error is more serious during peak periods. BiLSTM is in good agreement with the real situation during the stationary period and the low peak period, and it is slightly different from the real situation during the peak period, but it can still be used as a reference. In general, the prediction accuracy of the BiLSTM algorithm for traffic flow is relatively high. The comparison of evaluation indicators shows that the coefficient of determination value of BiLSTM is 0.795746 greater than that of LSTM (0.778742), indicating that BiLSTM shows a higher degree of fitting than the LSTM algorithm, that is, the prediction of BiLSTM is more accurate. The mean absolute percentage error (MAPE) value of BiLSTM is 9.718624%, which is less than 9.722147% of LSTM, indicating that the trend predicted by the BiLSTM is more consistent with the actual trend than that of LSTM. The mean absolute error (MAE) value of BiLSTM (105.087415) is smaller than that of LSTM (106.156847), indicating that its actual prediction error is smaller than LSTM. Generally speaking, BiLSTM shows advantages in traffic flow prediction over LSTM. Results of this study play a reliable reference role in the dynamic control, monitoring, and guidance of urban traffic, and congestion management.

The present research showcases the Indoor Energy-Saving Optimization Design of green buildings. This integrated approach synergizes a building’s Indoor Energy-Saving process based on the intelligent GANN-BIM model. The GANN-BIM model is driven by genetic algorithms (GAs), which include artificial neural networks (ANNs) along with building information models (BIMs). Building information modeling (BIM) is a technology that involves the modeling and management of digital representation of all forms of structural buildings. These intelligent models can be exchanged and extracted in the form of files and are mainly used for the designing and decision-making of a building. The BIM model is empowered as an intelligent technology by incorporating artificial neural networks (ANNs) and genetic algorithms (GAs). The main objective of the research is Indoor Energy-Saving by implementing an optimized design of green buildings. Green buildings can benefit from the GANN-BIM model’s ability to handle complex and conflicting design requirements while using less computational power during the evaluation of the proposed approach. There are a variety of new green building technologies being developed, but they all share a common goal: to reduce human health and environmental impact by maximizing energy, water, and other resource efficiencies; protecting occupant health; reducing waste; and decreasing pollution. The empirical results of GANN-BIM proved that the proposed model outperforms well in enhancing energy evaluation.

The traditional research method of user consumption behavior influence is too subjective in the selection of indicators at the time of research, which leads to poor referenceability of research results. To this end, the problems of traditional influence research methods are improved by proposing a method for analyzing the influence of theme restaurant interior design on user consumption behavior based on the spatial layout of the interior environment. On the basis of analyzing user consumption behavior patterns and determining factors affecting consumption behavior, the influence model of user consumption behavior is constructed and statistical indicators are selected to complete the research process. The simulation experiment proves that the research results of applying the method can effectively improve the restaurant operation and have practical value.

With the development of the social economy, people are paying more and more attention to decorative effects and the comfort and individual characteristics of decoration. To meet the increasingly high requirements of customers, many restaurants have begun to focus on the personalization of the dining environment, which is comfortable to build and focuses on the spiritual satisfaction and experience of customers during the dining process. In this study, a comprehensive analysis of digital image processing technology is performed to implement an automatic illumination system with improved performance for the restaurant interior design and embed the restaurant interior design with intelligence. The convolutional neural network (CNN) is employed in the automatic illumination system to develop the human body recognition model. After a test of its recognition accuracy, the parameters of CNN are optimized, and high recognition accuracy of 0.97 is achieved. Compared with other models, the process of training the designed model implemented in this study can finish in 40 minutes, and the performance has been well optimized. Moreover, the processing function of the model is also able to resist the interference of other external objects. The excellent automatic illuminating system can greatly improve the atmosphere as well as the service level of the restaurant at night, which can promote the modernization of the restaurant and give certain reference significance to the reform and advancement of the decoration industry.