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This study aims to augment the impact of dialect-related cultural elements among adolescents, thereby facilitating a more effective inheritance and progression of Chinese dialect culture within the contemporary socio-cultural milieu.Firstly, based on the LSTM architecture, the bidirectional long short-term memory-conditional random field (BLSTM-CRF) model framework for dialect recognition was optimized and established. Subsequently, employing the BLSTM-CRF model for dialect recognition and the acquisition of dialectal voice samples, the natural language sequence input underwent conversion into word vectors or character vectors. The word vectors or character vectors were input into the BLSTM layer to obtain the hidden state vector corresponding to each position, representing the context information of that position. The hidden state vector was input into a fully connected layer, and the CRF layer was adopted to calculate and find the highest scoring label sequence as the final prediction result. Secondly, the FBM theoretical model was utilized to analyze the target user needs through user research methods. Finally, a design framework oriented by dialect recognition and user needs was constructed, and interactive product design was carried out.On the basis of BLSTM-CRF, the product design framework is improved to complete the interactive application design.Integrating user needs and Long Short-Term Memory (LSTM) algorithms into the design of dialect products, we explore ways to preserve and disseminate dialect culture among the younger generation. This provides new insights for the digital inheritance and development of dialects, and also serves as a reference for the design process of related cultural apps.

Research on dialect protection: Interaction design of Chinese dialects based on BLSTM-CRF and FBM theories