This article describes a Python Flask‑based system that crawls tourism site reviews, processes the data with pandas, stores it in MySQL, and visualizes sentiment analysis, keyword frequencies, and rating trends through interactive ECharts charts on a responsive web dashboard.
This article demonstrates how to scrape attraction and food data for Changsha using Python, process it with pandas, visualize insights with Plotly and Pyecharts, and derive travel recommendations such as top scenic spots, popular eateries, and price trends.
This article walks through a Python-driven web‑scraping and data‑analysis workflow that collects, cleans, and visualizes tourism and food data for Changsha, revealing the most popular sights, highly‑rated eateries, and visitor preferences through interactive charts and word clouds.
This article walks through the complete process of designing, implementing, and visualizing a Python web crawler that extracts tourism hotspot data from ticketing sites for China's 2019 National Day holiday, covering requirement analysis, URL and element inspection, data collection, cleaning, and geographic heat‑map presentation using Pyecharts.
This tutorial walks you through using Python to collect tourism data from Qunar, extract key fields such as name, price, and rating, store the results in Excel with pandas, and visualize sales and popularity trends using pyecharts, including a simple recommendation algorithm.
This article explains what a tourism knowledge graph is, discusses its architecture, construction methods, practical applications such as QA and recommendation, and explores future directions integrating knowledge graphs with deep learning and multi‑domain fusion.
This article examines the distinctive features of tourism-domain knowledge graphs, outlines methods for constructing them from internal and external data sources, and explores their practical applications such as question‑answering bots, personalized recommendation, and advanced search within the online travel sector.
This article provides a comprehensive overview of augmented reality (AR) technology, explaining its fundamentals, differentiating it from VR, MR, CR, SR, and ER, and exploring its market trends, diverse industry applications—from marketing and social interactions to gaming, cultural tourism, and its connection with AI.
This article describes a tourism recommendation system that addresses data sparsity, seasonality, and geographic variations by using an offline‑online architecture, GBDT+LR CTR prediction, exponential decay scoring, and extensive feature engineering, achieving a 1.6% conversion‑rate increase and high accuracy and recall.
The article outlines Meituan‑Dianping’s tourism recommendation system, detailing its evolution from simple hot‑sale recall to sophisticated decay‑based, GPS‑aware, collaborative filtering and XGBoost reranking pipelines, the four‑layer architecture supporting dozens of travel scenarios, and future plans to broaden recall, adopt deep models, and expand multimodal travel recommendations.
The article examines the current state and projected growth of the virtual reality (VR) tourism market, outlining its key characteristics, market size, dominant applications, business models, content creation challenges, and the strategic role of high‑quality IP in driving future industry development.
