Cultural Heritage is a witness to human history, and its preservation has become a worldwide consensus. However, heritage crime is still rampant, and the protection of cultural heritages still needs excellent efforts. This paper analyzes the crime pattern of cultural heritage from the time series perspective and proposes a targeted early warning framework. The trends of the four heritage crime types were summarized on weekly, monthly, annual and decadal time scales. The upstream and downstream heritage crimes were strongly correlated on the daily time scale, with 90% of years correlated on the scale of 0.01. For different provinces and different types of cultural heritage crimes, a comprehensive early warning model framework is established using a combination of Prophet model prediction and qualitative analysis, and the optimal prediction horizon is given. The analysis results of this paper provide a viable path for applying heritage data to conservation work. It helps to prevent and control heritage crimes and provides ideas for the information construction of heritage security.

Heritage crimes can result in the significant loss of cultural relics and predicting them is crucial. To address the issues of inconsistent textual information format and the challenge of preventing and combating heritage crimes, this paper develops a system that extracts crime elements and predict heritage crime occurrences. The system comprises two deep-learning models. The first model, Bi-LSTM + CRF, is constructed to automatically extract crime elements and perform spatio-temporal analysis of crimes based on them. By integrating routine activity theory, social disorder theory, and practical field experience, the research reveals that holidays and other special days (SD) perform a critical role as influential factors in heritage crimes. Building upon these findings, the second model, LSTM + SD, is constructed to predict excavation-type heritage crimes. The results demonstrate that the model with the introduction of the holiday factor improves the RMSE and MAE by 6.4% and 47.8%, respectively, when compared to the original LSTM model. This paper presents research aimed at extracting crime elements and predicting excavation-type heritage crimes. With the ongoing expansion of data volume, the practical significance of the proposed system is poised to escalate. The results of this study are expected to provide decision-making support for heritage protection departments and public security authorities in preventing and combating crimes.

Tangible cultural heritage is vulnerable to various risks, particularly those stemming from criminal activity. Through analyzing the distribution and flow of crime risks from a spatial perspective based on quantitative methods, risks can be better managed to contribute to the protection of cultural heritage. This paper explores and summarizes the spatial characteristics of crime risks from 2011 to 2019 in China. Firstly, the average nearest neighbor (ANN) and the Jenks Natural Breaks Classification method showed that the national key protected heritage sites (NPS) and crime risks exhibit clustering features in space, and most of the NPS were located in the middle and lower reaches of the Yangtze River and the Yellow River. Secondly, the economy has no impact on crime risks in the spatial statistical analysis. However, the population density, distribution of NPS, and tourism development influenced specific types of crime risks. Finally, Global Moran’s I was used to examine the strong sensitivity between crime risks and cultural relics protection policies. The quantitative results of this study can be applied to improve strategies for crime risk prevention and the effectiveness of heritage security policy formulation.

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In recent years, driven by the huge profits in the illegal cultural relics circulation market, smuggling and excavation of cultural relics have been repeated, and the situation of heritage crimes has become more and more serious. It is important to understand the occurrence pattern of excavation-type heritage crimes and construct a time-series prediction model of excavation-type heritage crimes to prevent them. This paper uses the random forests algorithm to construct a time-series prediction model of heritage crimes, which effectively solves the problem of poor timeliness of traditional prevention methods and is an attempt in the field of heritage crimes prediction. This paper constructs a time-series data of heritage crimes at several time scales and finds that the model has the best prediction effect when the time step is set to 30. It suggests that there may be a certain pattern of occurrence of excavation-type heritage crimes at the monthly scale. The findings of this paper are expected to provide decision support for the deployment of prevention and control resources for protected heritage units.