Abstract: With the promotion of data as a production factor, traditional query pricing methods face tremendous challenges in practical applications due to their overly strict premise assumptions, limited support for flexibility and dynamics, and inadequate consideration of key factors. To address these issues, we innovatively design a query pricing algorithm based on the labor game model. This algorithm models the participants in data transactions as labor unions and employers, treating the data trading platform and data buyers as the labor union and employers, respectively. The data trading platform (labor union) is responsible for the fair and transparent calculation of the value of each traded dataset (wages), aiming to facilitate transactions as much as possible. Data buyers determine the purchase quantities of datasets based on their estimated value, personal needs, and budgets, thereby achieving a pricing strategy that balances the interests of all three parties. Experimental results demonstrate that compared to the popular Stackelberg game model, our algorithm better accommodates the interests of all parties and ensures greater fairness. Compared to traditional query-based data pricing methods, our pricing algorithm is more practical, offers greater flexibility and dynamics, and can dynamically adjust prices in response to changes in query results. The time complexity of our pricing algorithm is O(N), where N is the number of datasets related to the query, and it also guarantees no arbitrage.