The invention provides an auxiliary learning method and system for signal characteristic identification of a nuclear power plant-loop component loosening diagnosis system. The method comprises: operating parameters of a plurality of time points are calculated; the operating parameters of the plurality of time points are mapped to a high-dimensional vector space respectively; state tags are added to the plurality of time points respectively; the plurality of time points are classified in the high-dimensional vector space; an operating parameter of a new time point is inputted and calculated and the operating parameter of the new time point is mapped to the high-dimensional vector space; if a vector space distance between the new time point and any time point with the state tag added is smaller than a set threshold value, the new time point is determined to represent a known event; and if the vector space distance is larger than or equal to the set threshold value, alarming is carried out and thus a field engineer can be warned to check a nuclear power work log record of the new time point or carry out field checking on equipment. In addition, the invention also provides an auxiliary learning system. With the method and system, the practical determination experience of the engineer can be fused and thus the alarm parameter can be optimized.