The purpose of this paper is to build an analysis model of digital economy collaborative governance by using backpropagation (BP) algorithm driven by big data, to evaluate the economic development level of the city, and to provide suggestions for the future development of the city. This paper aims at the economic development level of eight urban areas in S city and compares the economic development level of each urban area from 13 basic indicators in four aspects: economic benefit, innovative development, people’s life, and sustainable development. This paper constructs a three-layer BP neural network model, scores the economic development level of each urban area, and puts forward a reasonable plan for the future urban construction of S city according to the comprehensive score. The results show that this research model has high accuracy in evaluating the economic development level of each urban area, which is consistent with the actual development situation. Therefore, the analytical model established in this paper utilizes the BP algorithm to precisely assess the urban economic development level. It offers optimized strategies for collaborative governance in the context of a big data-driven urban digital economy. This effective approach aims to achieve the collaborative governance objectives for urban digital economies, serving as an experimental reference for similar research in other cities.
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