## 1 Introduction
[17] introduced a multi-receptive field GCN into mechanical fault diagnosis and observed that the GCN can make full use of representational features to enhance the diagnostic accuracy. [PERSON] et al. [18] proposed a GCN-based diagnosis method, which achieved higher performance than the original diagnosis method by using hybrid metrics and prior knowledge. [PERSON] et al. [19] proposed a new adaptive propagation graph convolutional network model based on the attention mechanism, which is superior to the baseline model and can improve the accuracy of node- and graph-level classification tasks in downstream tasks. Graph construction in GCNs is a very important step, as the constructed graph structure determines the performance of the model [20]. At present, many graph construction methods have been proposed. [PERSON] et al. [21] combined an autoencoder with existing graph construction methods to propose a novel graph construction method. This proposed method uses the sigmoid activation function and the top-k order to identify the first k neighbors to complete graph construction. [PERSON] et al. [22] proposed a dynamic time warping (DTW) graph construction method. This method first calculates the DTW between samples; then, for the central sample, k neighbors with the smallest DTW are connected to construct the graph. [PERSON] et al. [23] used the Euclidean distance to determine the similarity between nodes; then, they used the top-k order to determine the k neighbors of nodes to construct the graph. In the above-mentioned methods, the measure of the similarity between nodes differs, while the principle of graph construction is the same, which is the concept of k-nearest neighbor (KNN) graph construction. This approach leads to only one hyperparameter k in the graph construction method, which has great limitations. In addition, this approach does not use the label information of the original data, resulting in unused information. Furthermore, although the GCN has achieved relatively favorable results, its adjacency matrix is composed of zero and one, which signifies that for the central node in the graph, every neighbor is equally important. This makes it impossible to pay sufficient attention to key nodes and limits the expressive ability of the GCN. To address the above problems, this paper proposes a dynamic multi-attention graph convolutional network (DMGCN) based on adaptively constructed graphs called AKNN-DMGCN for mechanical fault diagnosis of HVCBs. First, a new dynamic adaptive k-nearest neighbor (AKNN) graph construction method is proposed to construct the graph. The AKNN method has high fault tolerance to noise signals and can construct a graph structure with rich and accurate information, which can fully utilize the original data information and enhance the overall performance of the method. Then, DMGCN is adopted to mine the numerical and structural information of the graph for classification. Dynamic multi-attention [24] ensures that the method assigns attention coefficients according to the condition of each node, which improves the expressive power. According to the experimental results, AKNN-DMGCN exhibits high performance on both balanced and imbalanced datasets and can achieve better diagnosis of HVCB mechanical faults than traditional methods. The main achievements of this study are as follows:  
1. Graph construction is an important step in the diagnosis process of GCNs. This paper proposes a new AKNN graph construction method that is able to take full advantage of the numerical and label information of the original data and assign different k according to different node conditions, allowing the graph to contain richer information. 2. A dynamic multi-attention mechanism is added to the GCN to form DMGCN. To the best of our knowledge, this is the first time that DMGCN is used in the mechanical fault diagnosis of HVCBs. DMGCN not only adds a multi-attention mechanism to the GCN but also achieves the transformation from static attention to dynamic attention. 3.