With the increasing volume of solar radio spectrogram observation data, deep learning-based recognition and detection of solar radio bursts have become a key research direction. However, most studies on meter-wave solar radio spectrogram recognition and detection rely on proprietary datasets, and publicly available datasets remain scarce. To address this issue and facilitate the use of public datasets for deep learning model validation, thereby promoting the development of automatic detection methods for meter-wave solar radio spectrogram, we propose a new solar radio spectrogram dataset. The dataset is constructed from meter-wave solar spectrogram observation data from the Learmonth Observatory in Australia and the meter-wavelength observing system of the Chashan Solar radio Observatory (CSO) of Shandong University. Experimental results demonstrate that the proposed dataset effectively supports the recognition and detection of solar radio spectrograms, providing essential data resources for the intelligent recognition of solar radio burst features.