Then, the weight coefficient of vibration feature vectors is weighted based on the fuzzy analytic hierarchy process, and the combined eigenvalue is calculated by feature vectors and fuzzy weights. The integral eigenvalue is used as the input vector, and the SABO algorithm is applied to enhance the probabilistic neural network to train the vibration signal for diagnosis. In Ref. [17], the distribution of harmonics and the fundamental wave ratio are used as the feature information of a 110 kV power transformer's winding looseness fault. The SHapley Additive exPlanations (SHAP) method is introduced to analyze the constructed feature information, and the key feature information combination set is generated. Finally, the high-accuracy identification of transformer winding looseness is achieved. As deep learning models have advanced rapidly in the area of image recognition [18; 19; 20], the one-dimensional vibration signal is converted into a two-dimensional image, and the convolutional neural network is applied for feature extraction [21], so as to achieve image classification and recognition, which can effectively improve the accuracy of fault recognition. Common two-dimensional image generation methods mainly include the time-frequency analysis [22], wavelet transform [23], and image coding methods. Image coding technology mainly includes Markov transition fields, Gram-angle field transformation, and recurrence plots. The two-dimensional images generated by these methods can fully express the characteristic information of one-dimensional vibration signals. Ref. [24] proposes a transformer winding loose fault diagnosis method based on Gram-angle field transformation and transfer learning-AlexNet. The two-dimensional image set of the Gram-angle field of transformer vibration signals is generated by the sample construction method [25]. The generated image set is input into AlexNet for transfer learning, and the optimized neural network fault diagnosis model is obtained. In summary, at this stage, there are still the following limitations in the identification of transformer winding faults: (1) The sample data of winding looseness faults under multi-load transformer current conditions are lacking, and the adaptability of fault diagnosis models is not strong. (2) The fault identification of single measuring points on the outer surface of the transformer box is not universal. (3) The time correlation is lost in the extraction of two-dimensional feature map information of vibration signals. The traditional convolutional neural network model is prone to overfitting in the training of transformer windings' loose fault feature maps, which poses a significant challenge for improving the recognition accuracy. In view of the limitations in the above-mentioned research on transformer winding fault identification, this study makes the following improvements and breakthroughs: (1) Vibration signals of winding looseness under different load current conditions of the transformer are measured to enhance the adaptability of the fault diagnosis model. (2) Six vibration acceleration sensors are uniformly arranged on the surface of the transformer tank to eliminate the contingency of fault identification from a single measurement point. (3) A method for generating time-domain vibration signal feature maps using relative position matrices is proposed to preserve the time correlation of vibration signals, and the ConvNeXt model is employed to address the overfitting issue of traditional convolutional neural network models during map training. The remaining sections of this paper are organized as follows: Section 2 introduces the transformer winding loosening test and analyzes the characteristics of vibration signals under winding loosening faults. Section 3 presents a method for constructing two-dimensional feature maps based on the relative position matrix and Gram-angle field transformation to generate time-frequency-domain vibration feature maps for different winding loosening fault states of the transformer.