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In this paper, 41 prefecture level cities in the Yangtze River Delta (YRD) are selected as the research objects. Based on the semantic mining model, the policy data information from three dimensions namely national, provincial and municipal levels is integrated to build data-driven multiple scenarios. Then the long short-term memory neural network optimized by particle swarm optimization (PSO-LSTM) is further used to predict the peaking time of the YRD urban agglomeration under multiple scenarios. The results show that the YRD urban agglomeration as a whole can achieve carbon peaking on time under all three simulation paths, but the peaking situation of specific cities within the region will be significantly different due to differentiated policy orientations. Under the business as usual scenario, some cities in the north of the region cannot achieve carbon peaking by 2030. Under the stable growth scenario, more cities cannot achieve carbon peaking on time. However, under the low carbon scenario, all cities within the region can achieve carbon peaking by 2030. Therefore, when exploring effective carbon peaking paths, adopting more stringent policies and measures to limit carbon emissions can help achieve the regional carbon peaking goal earlier, ensure that all cities within the region can achieve carbon peaking on time, and can also narrow the gap between cities within the region in terms of peaking time. This paper explores a new scenario analysis parameter setting and carbon emission prediction method, which overcomes the subjectivity of current scenario analysis research in parameter setting to a certain extent, and also provides a scientific reference for predicting the diversified development trend of carbon emission. • The semantic mining method is used to determine the scenario parameters. • PSO-LSTM is used to improve the accuracy of carbon emission prediction. • Multiple scenarios of carbon emission development trends. • Differentiated emission reduction paths for cities. [ABSTRACT FROM AUTHOR]