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Since demand forecasting is the first step in managing and operating a tourism business, its accuracy is very important to tourism businesses. In order to address NN's drawbacks, such as local optimization, slow convergence, and large sample sizes, this paper organically combines the PSO and NN models and builds a PSO-NN-based tourism demand forecasting model. The tourism demand forecasting indexes, the choice of NN forecasting models, the modelling process, and the implementation methods are first analysed and studied along with the fundamental theories and forecasting techniques of PSO and NN. In order to increase the precision of the prediction model, the PSO algorithm is also used to optimise the weights and thresholds of the NN. The final section of the paper compares the performance of the model developed in this paper with the most widely used model for forecasting tourism demand. According to the experimental findings, this model's prediction accuracy can reach 95.81 percent, or about 10.09 percent higher than the prediction accuracy of the conventional NN model. There are some practical implications to this research. Applying the optimization model to the forecast of tourism demand is doable and practical. [ABSTRACT FROM AUTHOR]

With the progress of science and technology and the arrival of the big data era, people increasingly rely on computers to deal with daily life and related affairs. In recent years, machine learning has become more and more popular and has achieved good results in some fields, which also makes machine learning widely used. Among them, visual neural network technology can more intelligently analyze the emotional expression of oil painting, which is one of the current research hotspots, involving machine vision, pattern recognition, image processing, artificial intelligence, and other fields. However, in the art field, oil painting is still very different from other images. At present, there is no deep learning algorithm to identify the application of emotional expression analysis in oil painting theme creation. This paper will start with the neural network algorithm and combine the big data recognition technology to analyze the emotional expression of the oil painting subject in the public environment and establish the emotional expression analysis model of oil painting creation based on big data and neural network. The experiment shows that the graphics synthesized by this model have high resolution and good definition, but the speed is slow in the process of experimental operation. It takes about one hour to complete a round of image optimization. [ABSTRACT FROM AUTHOR]