The monitoring and analysis of massive data from radio spectrum monitoring are essential components of radio regulation work. To address this, the paper proposes a lossless compression method based on wavelet-like transform for radio spectrum monitoring data. This method first converts the one-dimensional spectrum signal into a two-dimensional matrix based on temporal correlation. Once transformed into a two-dimensional matrix, there is redundancy in both the horizontal and vertical directions. The algorithm employs a convolutional neural network to replace the prediction and update modules in traditional wavelet transform, and introduces an adaptive compression block to handle features of different dimensions, thereby obtaining a more compact representation of spectrum data. Furthermore, the paper designs a context-based deep entropy model, which utilizes the wavelet-like transform's different subband coefficients to obtain entropy coding parameters, estimating cumulative probabilities to achieve spectrum data compression. Experimental results indicate that the proposed algorithm achieves additional performance improvements compared to existing traditional lossless compression methods for spectrum data, such as Deflate. Moreover, when compared with typical two-dimensional image lossless compression methods like JPEG2000, PNG, and JPEG-LS, the proposed method achieves over 20% better compression effectiveness.