This database system integrates publicly disclosed structured information on scenic spots from mainstream Chinese online travel platforms, covering multi-dimensional attribute data of over one million tourist attractions across 31 provincial-level administrative regions. Core fields include city name, attraction name, attraction grade, attraction ranking, attraction tags, popularity rating, user rating, review count, market price, actual price, admission information, administrative region, detailed address, official phone number, introduction, opening hours, cover image, and attraction images—totaling eighteen key variables. This dataset provides high-granularity foundational data support for empirical research in tourism economics, regional cultural tourism resource evaluation, optimization of attraction management decisions, and commercial investment analysis. Its detailed price comparison system and user feedback indicators offer significant value for revealing micro-level operational mechanisms in the tourism market.
Data Uniqueness:
- Quantified Consumer Behavior Metrics: Exclusively integrates three market feedback dimensions: popularity ratings reflect platform traffic orientation; user ratings represent satisfaction measurements with large sample sizes; review counts indicate user engagement with high completeness.
- Empirical Database of Dual-track Pricing: Precisely captures pricing strategy differences by recording both market prices (official listed prices) and actual prices (transaction prices at consumption endpoints).
- Spatial-Operational Linkage Mechanism: Deeply structured geographic information presents three-tier administrative divisions (province-city-district). Operational rules are computable—opening hours support temporal distribution analysis; admission information enables policy tagging (e.g., reservation systems/visitor limits).
- Multimodal Resource Integration: Transforms unstructured data into assets—attraction tags enable AI-driven thematic clustering (2-3 tags per attraction on average); cover/attraction images facilitate landscape feature analysis via image recognition; introduction texts support keyword frequency analysis.
Data Application Value:
- Academic Research: Supports spatial analysis of regional tourism economies (e.g., modeling resource agglomeration effects using popularity ratings and GIS data), empirical studies on attraction price elasticity (calculating demand elasticity coefficients via market-actual price deviation rates), and cultural heritage resource evaluation (analyzing multidimensional tag semantics via NLP techniques).
- Industrial Decision-Making: Provides site selection assessments for cultural tourism investments (identifying high-potential attractions via user rating matrices and tag clustering), data foundations for smart attraction development (analyzing correlations between admission policies and service facilities), and destination marketing optimization (building Vector Autoregression models between popularity fluctuations and visitor flows).
The CnOpenData Scenic Spot and Rating Database standardizes multi-dimensional attraction information from leading travel platforms, establishing China's most comprehensive public dataset of attraction attributes. This data serves as an authoritative source for regional economic development evaluation, commercial investment analysis, and industrial policy formulation.
Time Coverage
As of June 2025
Data Scale
Field Display
Sample Data
Data Update Frequency
Annual updates