With the continuous expansion of speech synthesis application scenarios, the demand for multi-speaker and multi-emotion speech synthesis is increasing. In practical applications, there is often a need to synthesize speech signals with specific styles. To address this, an end-to-end speech synthesis model based on the attention mechanism is proposed. First, a speaker encoding module is designed to extract speaker feature representations from speech signals using the attention mechanism, combined with dataset features such as gender and age labels to construct a speaker feature database. Second, a style encoding module is designed to assign different levels of attention and weights to different Mel feature channels using the attention mechanism. Then, virtual speaker features are constructed using K-nearest neighbors, allowing for the flexible combination of different speaker and style features to synthesize voice with specific characteristic styles, even without requiring real speaker data. Experimental results show that this model has a faster training speed compared to the SV2TTS model and can synthesize high-quality speech with specific styles.