There has been a noticeable uptick in the value of data assets across a number of industries, thanks to the expansion of the digital sector and the widespread adoption of big data applications. Recognising the current state of growth, the country and relevant firms have made it clear that supporting the development of data elements is crucial. Asset evaluators see data asset value assessment as crucial to the data industry's long-term viability and growth. Consequently, studies examining the worth of data assets will offer expert assurances for the growth of the social economy. This article presents a data asset value evaluation model that uses the BP neural network (BPNN) algorithm for analysis and quantification of data asset value. This study builds an assessment index system for data asset value based on the various data asset valuation models and the structural stage theory of data development. It then conducts an objective analysis of the elements influencing data asset value. In addition, the essay delves into the workings of BPNN by exploring their capacity to model nonlinear interactions and by employing tools to set up a data asset value assessment framework. After all the necessary steps were taken, including data collection and preprocessing, model training and optimisation, and experimental validation, the data asset value evaluation approach presented in this research outperformed the conventional asset evaluation method.