To address the interpretation errors caused by peak shape distortion in traditional peak value methods for spot tracer correlation logging under polymer flooding and low water injection conditions, this study focuses on the low-permeability reservoirs in Daqing Oilfield, aiming to establish a multi-method collaborative interpretation system suitable for complex injection conditions, thereby enhancing the identification accuracy of thin-poor waterflooding layers and the dynamic monitoring capability of low-injection-rate wells. Through comparative analysis of theoretical models and adaptability boundaries of peak value, centroid, center-of-mass, and correlation methods, a multi-method collaborative interpretation framework based on tracer transport dynamics is proposed. First, a tracer peak sharpness classification evaluation system is constructed, and baseline dynamic correction algorithms (wavelet-Kalman joint filtering) are applied to preprocess logging curves. Subsequently, a “peak type-interpretation method” matching matrix is established to quantify optimal method selection rules for curves with varying morphologies. Validation experiments in Daqing Putaohua Oilfield demonstrate that the multi-method collaborative strategy effectively overcomes the theoretical limitations of single methods. The proposed peak sharpness classification and baseline dynamic correction techniques break through the morphological adaptability constraints of traditional methods, achieving an inter-layer water absorption rate matching error of less than 5% compared to isotope logging data and improving the compliance rate of water absorption profile monitoring in low-injection-rate wells (3m3/d) to 92%.The developed dynamic monitoring interpretation standard for low-permeability reservoirs provides a logging interpretation solution with an error rate below 5% for unconventional hydrocarbon reservoir development. This methodology has been scaled up in Daqing Oilfield, delivering significant economic benefits.