## 1 Introduction  
With the development of Internet communication technology, the channels through which people can share and receive information have also been greatly enriched. From traditional texts and images to the rapid development of short videos, many short video platforms such as KUAISHOU, YouTube and TikTok have appeared globally. Since entering the era of Web 3.0, all Internet products pay attention to speed and efficiency, and content auditing has become a key problem. When users upload videos, the platform often needs to classify videos according to the content sent by users, so as to help the platform better recommend relevant videos to users in the later stage. Based on this, many researchers have carried out research on the domain recognition of speech, topic identification or event recognition detection. According to the information dissemination model SIR and Chramm's Model, we can understand that the information sender and the information receiver must have a common field of experience in order to facilitate the transmission of information. In this experiment, a large amount of unlabeled data will be used and labeled data will be used for pattern recognition, and semi-supervised learning (SSL) will be used to improve the accuracy and speed of learning. Sound event detection has become a hot topic in recent years, and is widely used in complex environments, such as smart homes, driverless, urban public security, etc. The duration of complex audio is usually between a few minutes and a few hours, and the average duration is much longer than that of simple audio. Due to data imbalance, background noise can easily overwhelm target audio events, and non-target audio events can confuse detection results, resulting in performance degradation. Usually, automatic speech recognition (ASR) is used as the first stage of speech topic recognition. As short videos have the problems of lax content, high noise and more redundancy, when acquiring speech features Mel Frequency Cepstrum Coefficient (MFCC) after processing such as mute excision and noise processing, we introduce the attention mechanism Transformer to select the most relevant data to the target and transform the speech into a higher quality feature subset to deliver signals to the downstream model. In the past, typical networks such as the Convolutional Neural Network (CNN) convoluted the image or voice and then pooled it. This process easily leads to the loss of information. Therefore, in this task, in order to expand the receptive field and keep the information from being lost, we propose to extract multi-dimensional features by Causal Hole Convolution, which not only ensures that the information in the transmission process is not distorted, but also makes the feature engineering processing easier and more convenient. It can be used for input and output parameters of different time lengths. Next, a support vector machine (SVM) recognizer is used to match the features of the incoming data to be classified, recognize the audio theme and output it. The contributions to this paper are: (1) the use of challenge on UrbanSound8K and audio from AudioSet database for sound category training, and the combination of the ability of the Causal Dilated Convolutional Network (CDCN) to handle perceptual fields with the ability of Transformer to focus on contextual information. The final result is semi-supervised training for sound event detection. (2) From the perspective of audio randomness, the audio classification method based on the attention mechanism is proposed, and two mechanisms are designed. The first one is a time-frequency attention mechanism on the time domain, and the second is time-frequency attention on the spatial domain, and the theoretical analysis of the two attention mechanisms is carried out. (3) The features are processed using different extracted features, such as the MFCC, log-Mel spectrogram, and different dimensionality reduction methods.