At present, ecological environmental protection and rational use of natural resources have become an important strategy for national development, and the establishment of a scientific system for calculating the damage compensation of natural resource assets is of great significance for promoting the construction of ecological civilization. In this paper, we constructed a natural resource asset damage compensation value assessment model based on IWOA-BP neural network, and established an accounting index system including land resources, biological resources and water and air regulation functions. The study adopts the improved whale optimization algorithm to optimize the weights and thresholds of the BP neural network, improves the uniformity of the initial population distribution through Sine chaotic mapping, and constructs a comprehensive accounting method for the value of land assets, the value of biological assets and the value of water and air regulation assets. The validity of the model was verified with Yulin City as the empirical research object. The results show that the IWOA-BP model is stabilized after 77 iterations, and has a faster convergence speed and higher prediction accuracy than the traditional BP, GWO-BP, and WOA-BP models. The average relative error of the model is controlled within 9.7452%, the average absolute error is 0.3013, and the root mean square error is 0.2241, and the assessment accuracy is significantly better than other algorithms. The total value of natural resource assets in Yulin City increased from 577,452.16 billion yuan to 577,934.63 billion yuan, and the value of land resources accounted for 64.94%. The model can effectively solve the problems of low accuracy and slow convergence of traditional methods in the calculation of natural resource asset damages, and provides a scientific and feasible calculation method for natural resource asset damages.
