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This study aims to solve the problems of traditional indoor car search positioning technology in terms of positioning accuracy and functionality. Based on database technology and deep learning technology, an LSTM model with attention mechanism was established. This model can simultaneously extract temporal and spatial features, and use attention mechanism for feature importance recognition. The entire positioning model has been designed as a triple functional entrance that includes positioning, car storage, and reverse car search, enhancing the user's coherent experience. The data results show that the root mean square error of the LSTM (Attention) model designed in the study is 0.216, and the variance is 0.092. Among similar positioning models, the index value is the smallest, while the CDF line rises the fastest and the maximum value is the highest. The research conclusion indicates that the LSTM (Attention) indoor positioning model designed in this study has better computational performance and can help users achieve more accurate positioning and vehicle navigation.