Network technology influences and transforms the language use, environment, and cultural practices of ethnic minorities. This paper designs a text classification model for minority languages and cultures, utilizing BERT for word vector modeling, and introduces LSTM and attention mechanisms to capture distant semantic information, solve the gradient vanishing problem, and learn contextual details. The BiLSTM multi-head attention mechanism is then used to filter important semantic features of the input sequence, and finally, all extracted features are linked to complete the text classification and output the categories of minority language and culture texts. The model is applied to text datasets from ethnic minority forums and their news video datasets. It is found that the proportion of traditional ethnic minority vocabulary in forums decreased from 14.6% in 2004 to 10.7% in 2024. Conversely, the ratios of vocabulary related to consumption, marriage, employment, and education increased, with the ratio of marriage-related vocabulary rising from 0.33 to 0.94. This suggests that, despite the influence of Internet technology, traditional cultural concepts of marriage among ethnic minorities remain widely observed. This study provides a feasible solution for categorizing texts in minority languages and offers a unique reflection on the influence of the Internet on the cultural changes of minority languages.