In practical applications, control valves are often operated under multiple control modes, causing the same fault in valves to exhibit distinct characteristic information under different control conditions. This makes the machine learning based diagnostic models trained by a single-condition dataset difficult to generalize and leads to performance degradation. To address this problem, a multi-condition fault classification method is proposed in this article by combining the quantum attention-based sparrow search algorithm (QA-SSA) with an elastic adaptive random forest (EARF) model. The proposed EARF model is formulated on the adaptive random forest (ARF). By introducing a two-level decision mechanism, optimizing the placement of the global drift detector, implementing a local pruning strategy, and dynamically adjusting the number of trees, EARF reduces the computational cost of ARF, improves the modeling efficiency and diagnostic accuracy, and enhances the adaptability to variant operating conditions. To optimize the coupled hyperparameters of EARF model, QA-SSA optimization algorithm is designed by introducing quantum behavior and a Boltzmann selection strategy into the traditional SSA. The algorithm improves the searching efficiency and robustness in high-dimensional hyperparameter spaces. Finally, simulative experiments are implemented on the electric control valve fluid control system in our laboratory to evaluate the proposed method classifying six types of faults under flow, pressure, and level control conditions. The results show that the proposed QA-SSA-EARF method achieves a classification accuracy 97.47% under a single condition case, which is 9.65% and 3.64% higher than the optimized random forest and ARF models, respectively. In a multi-condition case, the average classification accuracy is 93.12% by the proposed method, which is 2.59% and 8.9% more than the other two methods. Therefore, the effectiveness and robustness of the proposed approach are verified for fault diagnosis tasks in multi-operating conditions.