A soundscape is a sound environment of the awareness of auditory perception and social or cultural understanding. To improve the subjective initiative of the Chinese classical music students, this study explores new learning modes and methods using soundscape and investigates its learning effect using intelligent music software. To examine the emotional experience of players in playing before and after learning, 50 students from music majors and 50 from non-music majors were selected. Results show that in the positive and negative emotional classical music experiment, most music majors and non-music majors have weak emotional experience at the beginning, and only a few have a strong emotional experience, which could reach 6 points. In the second scoring, most majors have a score of about 7 points, indicating strong emotional experience and a few have a score of about 4 points and 9 points, representing that there were relatively few majors with weak emotional experience and strong emotional experience. The overall emotional experience score is low in the comparison between non-music majors and music majors, and the second score in the entire experiment is significantly higher than the first score, signifying that the learning effect of players is obvious, and intelligent music software and soundscape play a role in the exploration of Chinese classical music.

Music has become the main information carrier, and music and its emotional expression are accurately classified to obtain relevant information. However, how to classify music accurately is a problem that needs to be discussed. The concept and feature extraction strategy of the morphology of music are described. Moreover, the feature extraction and morphological classification elements of digital music are introduced. Next, music morphology is recognized and classified based on the neural network and relief algorithm. In the network, by randomly selecting different music types, the audio data is input into the neural network as the original data and processed by the relief algorithm. The classification and recognition accuracy of the Relief algorithm are verified by changing the number of iterations. The results show that the model’s classification accuracy based on the number of iterations is 78.958%. Then, the traditional statistical analysis classification method’s performance is compared with the proposed model. The recognition accuracy of the model proposed reaches 92%, which shows that the model can effectively classify music morphology. This study provides a theoretical basis for music morphology recognition in the wireless network environment.