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Policy intensity is a crucial determinant of policy effectiveness. Analysis of policy intensity can serve as a basis for policy impact evaluation and enable policymakers to make necessary adjustments. Previous studies relied on manual scoring and mainly addressed specialized policies with limited numbers of texts. However, when dealing with text-rich policies, the method inevitably introduced bias and was time-consuming. In this paper, we propose a procedure facilitated by machine learning to analyze the intensity of not only specified but also comprehensive policies with large amounts of texts. Our machine learning-based approach assigns scores to the policy measure dimension, then cross-multiplies with two other dimensions, policy title and document type, to calculate intensity. The efficacy of our approach was demonstrated through a case study of China's environmental policies for livestock and poultry husbandry, which showed improved performance over traditional methods in terms of efficiency and objectivity. [ABSTRACT FROM AUTHOR]