The invention discloses a fouling experimental device, provides a particulate fouling resistance prediction method for an arc-tube heat exchanger, and further discloses a corresponding prediction system by utilizing the object-oriented high-level language Delphi. The prediction method comprises establishment of the fouling experimental device, determination of experimental tubular products, determination of geometric dimensions, installation of a measurement and control unit, measurement and processing of parameters, establishment of prediction models, optimization of important parameters, and establishment and application of judgment models and the prediction system. According to the particulate fouling experimental device, the prediction method and the prediction system for the arc-tube heat exchanger, the defect of a local minimum of a neural network and other conventional methods is overcome, the phenomena of under-learning and over-learning are effectively restrained, the problem of generalization in the machine learning theory is solved, the calculated amount is small, the model optimization speed is high, and online monitoring of fouling resistance of a heat-exchange device can be achieved. The most prominent advantage is that small samples can be used for training models. Due to the fact that the fouling characteristics of the heat exchanger are predicted by the models in terms of temperature, flow speed and other parameters which are easy to measure, a lot of manpower and material resources are saved, and a new method is provided for designing a cooling water system under a known water quality condition afterwards and predicting the fouling characteristics.