The measurement accuracy of dynamic weighing systems is easily affected by the coupling of multiple factors, leading to significant system errors. This paper addresses the issue that the individual responses of each quartz wafer within traditional quartz sensors are easily masked by the overall signal, making it difficult to conduct in-depth analysis of the error propagation mechanism within the sensor and thus challenging to effectively compensate for the accuracy of dynamic weighing systems. A 16-channel independent synchronous acquisition system for quartz wafers was designed and implemented, including quartz weighing sensors, charge amplifiers and synchronous acquisition devices, enabling the 16 quartz wafers that were originally connected together to be separately collected and analyzed for signals. Based on this experimental platform, a multi-source error decoupling and propagation model was proposed. By reconstructing the signals through an adaptive weighted fusion algorithm and optimizing the BP neural network with the ant colony optimization algorithm, the accuracy and robustness of the dynamic weighing system were significantly improved. Experimental results show that compared with the traditional model, the average relative error was reduced from 4.01% to 1.81%, and the maximum relative error was reduced from 10.26% to 2.22%. This provides new theoretical and practical references for high-precision dynamic weighing technology.