To leverage computer vision technology to improve the accuracy of tourism demand forecasting, a model based on deep learning with time series imaging is proposed. The model consists of three parts: sequence image generation, image feature extraction, and model training. In the first part, the tourism demand data are encoded into images. In the second part, the convolution and pooling layers are used to extract features from the obtained images. In the final part, the extracted features are input into long short-term memory networks. Based on historical tourism demand data, the model for forecasting future tourism demand can be obtained. The performance of the proposed model is experimentally assessed through comparing against seven benchmark models.

为了利用计算机视觉技术提高旅游需求预测的准确性，提出了一种基于深度学习的时间序列成像模型。该模型由三部分组成：序列图像生成、图像特征提取和模型训练。第一部分将旅游需求数据编码成图像。在第二部分中，卷积层和池化层用于从获得的图像中提取特征。在最后一部分，提取的特征输入到长短期记忆网络中。根据历史旅游需求数据，可以得到预测未来旅游需求的模型。通过与七个基准模型进行比较，对所提出模型的性能进行了实验评估。