As a quasi-public product, fishery insurance has become an important starting point for the construction of the modern fishery industry chain, supply chain and value chain risk management mechanism. We used visual data processing methods and text mining technology to screen policy samples. We then built a fishery insurance policy evaluation system based on the Policy Modeling Consistency (PMC) index model. We combined the PMC index score and PMC surface to quantitatively analyze the policy samples. This paper has four important findings: (1) After three adjustments and developments, the fishery insurance policy has grown in terms of initial attention, changes, and development and gradually matured. (2) A gap exists between the content of the fishing insurance policy text and the actual demand. The scoring results of the policy samples are concentrated in the acceptable range, the policy effects are not satisfactory, and the formulation of fishery insurance policies has weak links that need to be improved. (3) The consistency and effectiveness of fishery insurance policies have developed simultaneously with fishery insurance research, and the practical effects of high-quality fishery insurance policies are conducive to the development of theoretical research. (4) The policy text of fishery insurance has major problems, such as missing joint force of issuing institutions, low professionalism of the text, inadequate subdivision guidance of fishery insurance, weak social effectiveness, high dependence on financial subsidies, lack of incentive sustainability and corresponding laws and regulations and reduction in policy feasibility among others. Considering the above issues, this paper puts forward relevant policy optimization paths and safeguard measures on the basis of giving priority to greater absolute value.
Evaluation on the Effect of Fishery Insurance Policy: Evidence Based on Text Mining
