Aggregation model, a business model in which an aggregation platform attracts a large number of third-party platforms to gather by leveraging the benefits of cost savings and guaranteed order flow. This model has found widespread application in sectors like transportation, delivery, payment, digital content and leasing. Amap, Meituan, Baidu and DiDi are typical aggregation platforms in the taxi-hailing sector, as well as Cainiao, Huishouqian, and Zhuamob in other industries. However, the commission structure within this framework has become a focal point of public scrutiny, leading to penalties for several platforms due to perceived unreasonable commission rates. The intricate relationship between aggregation platforms and the third-party platforms they connect is a complex blend of competition and cooperation. This dynamic makes collaborative commission pricing an especially challenging task. Furthermore, the randomness of user demand, often influenced by external factors like weather, climate, and political events, adds another layer of complexity to platform commission strategies. These phenomena raise many significant problems that deserved to be explored. This article aims to address the issue of commission pricing in different supply-demand states caused by fluctuations in consumer demand. It seeks to answer questions such as: What commission strategies should platforms adopt under varying supply-demand conditions? How do optimal commission rates evolve over time? And can the implementation of dynamic commission rates effectively balance market supply and demand? Drawing upon two-sided market theory and optimal control theory, this paper establishes a dynamic commission pricing model that accounts for three distinct market states: decaying consumer demand, demand surge, and stable demand. By assuming single-homing behavior among users, the study delves into the dynamics of commission pricing, aiming to provide insights into how platforms can optimize their commission strategies in response to changing market conditions. Furthermore, we expand our fundamental model to incorporate scenarios where consumers exhibit multi-homing behavior. The insights gained from this study are profound and have significant implications for platforms operating within the aggregation model. Here are some key findings and their corresponding managerial implications: Dynamic Commission Pricing Strategies: Both aggregation platforms and their embedded third-party platforms can effectively adjust their supply and demand relationships and optimize their expected returns by adopting dynamic commission pricing strategies. This suggests that platforms should be proactive in adjusting their commission rates to mitigate potential losses from supply-demand mismatches. Impact of Competition: The intensity of competition among platforms, both in terms of quality and commission rates, significantly influences their expected profits, consumer surplus, and social welfare. As competition intensifies, platforms may be incentivized to lower their commission rates, which could potentially erode their profitability. Platforms should, therefore, carefully assess the competitive landscape and formulate pricing strategies. Benefits of Dynamic Commission Strategies in Fluctuating Markets: In markets with randomly fluctuating consumer demand, the implementation of dynamic commission strategies by both aggregator platforms and their accessing third-party platforms is beneficial for the overall development of the industry. Interdependence of Commission Rates: The optimal commission rates of aggregation platforms and their accessing third-party platforms are positively correlated, and the commission rate of the aggregation platform is more sensitive to changes in the commission rate of the accessing third-party platform. This underscores the need for platforms to consider the interconnectedness of their pricing decisions and those of their partners. Managerial Implications: Platforms should develop sophisticated demand forecasting models to anticipate changes in consumer demand and adjust their commission rates accordingly. Platforms should engage in strategic pricing discussions with their partners to ensure that pricing decisions are aligned with the overall interests of the ecosystem. Platforms should monitor competitive landscapes closely and adjust their pricing strategies to maintain competitiveness while optimizing profits. Platform managers should invest in data analytics capabilities to gain insights into market trends and consumer behavior, enabling them to make more informed pricing decisions.