Modeling and Analysis of Cybercrime Based on Feature Data-Driven

Aiming at the complex characteristics of cybercrime, this paper proposes a computer algorithm-driven quantitative analysis framework, integrating the Policy Text Quantification (PTQ) model, Difference-in-Differences (DID) algorithm, Spearman correlation analysis, and Poisson Regression (PR) algorithm to evaluate cybersecurity policy effectiveness and predict crime distribution. The PTQ model performs computer semantic parsing on policy texts to construct a quantitative system with 5 primary feature dimensions and 17 secondary indicators, while the DID algorithm validates the dynamic impact of policies on crime volume. The Spearman algorithm identifies strong correlations between technical measures and cross-domain cooperation mechanisms. The Poisson regression model analyzes 20 digital demographic features, revealing significant positive correlations between technical features and crime rates, with unemployment data positively driving crime. Experiments show that policy designs based on technical features are most effective in reducing crime, and algorithm verification confirms that policy implementation delays degrade model prediction performance. This study provides a multi-algorithm fusion technical path for computer modeling and intelligent prevention of cybercrime.