[23] uses the dilated CNN to identify the dynamic loads on a supported beam. Two CNN layer are used for extract features from the vibration responses, and a MLP with two layer would regress the time series loads. [PERSON] et al. [24] propose to use the time delay neural network (TDNN), which is equivalent to a one layer CNN for a sequential input, to identify the dynamic loads for an aircraft rudder model. [PERSON] et al. [25] apply a CNN with an attention mechanism to identify random loads on aircraft structures under noise disturbance conditions. The attention mechanism can better fuse features extracted by the network and improve the accuracy of load identification; however, the attention module would introduce additional parameters and computing costs. The RNN is used for load identification problems of nonlinear structures in [26]. [PERSON] and [PERSON] [20] compare the performances for load identification on different network architecture, including the MLP, the TDNN and the Elman neural network (ENN). The ENN is selected in [20]. The neurons in the ENN are designed with a feed-back branch, which shares some similarities with the RNN. [PERSON] et al. [27] study the dynamic load identification problem using the LSTM network. The LSTM is a variant of RNN with a gated unit, which is useful for the long-term dependencies problem in RNN. The method in [27] is verified by real data from a supported beam. While the above-mentioned intelligence algorithms mainly focus on the load identification in time domain, in the literature, approaches in other domains like frequency domain [16] and transform domain [28; 29] are widely applied in load identification as well, while showing good performances. Experimental results in [29] show that a better identification accuracy can be obtained in the transform domain than that in the frequency domain using a regularization-based method. In this paper, we tend to address the problem of dynamic load identification with an NN-based method in the transform domain, unlike the time-domain-based NN method [23; 25; 27]. The network would take the decomposed components of several responses in a time window in the transform domain, e.g., the wavelet domain, as the input. The deep convolution network is designed with weight normalization and batch normalization to extract transform domain features. The network would predict the load in the transform domain based on the extracted features. Similar inputs are also used in [30], in which an LSTM network is applied. While there is similarities between method in [30] and the proposed method, the amount of parameters in the CNN is smaller than that of the LSTM network in [30], which would be useful for the load-identification problem with a small-scale training dataset. The amount of input response signals of the proposed method can be flexible, i.e., a single response signal as the input or multiple responses. The outputs of the proposed approach are different from those in [23]. In [23], an MLP is utilized to regress the time-series of loads. Similar output designs are also used in [31; 32], in which the encoder-decoder networks are proposed for feature extraction in predicting the noise-canceling signals. The depthwise separable convolution (DSC) [33] is used in the CNN encoder-decoder architecture in [31] for reducing the amount of parameters. The DSC is also applied in [32], in which a channel attention is designed between the encoder and decoder network for better feature fusion, while a two-layer LSTM helps to learn the temporal representations with the consideration of time delay. In the proposed method, the output is designed to be the prediction of the load at the current time-stamp, rather than a time-series of loads. The output design in the proposed method is based on two considerations. First, since the proposed method is in the wavelet domain, the load signals are decomposed into different components, so to predict all decomposed components of time series of loads, a large MLP or a large encoder-decoder network is required.