Abstract: Approximate matching is an indispensable link in the normal use of multimodal data technology, but the process of approximate matching is vulnerable to data redundancy, heterogeneous components and other issues. Firstly, parallel wavelet algorithm is used to eliminate the noise in multimodal data to avoid the impact of noise on the matching process. Secondly, tensor decomposition clustering algorithm is used to divide the data with different similarity into different clusters to eliminate the data difference of different clusters. Finally, the preprocessed data is input into the data matching model based on spatial direction approximation, The approximate matching of multimodal data is completed by calculating the spatial direction approximation and editing the distance between the reference data and the data to be matched. The experimental results show that the proposed method has high matching precision, high recall and short matching time. 

Key words: dual input output parallel architecture, data difference, tensor decomposition, spatial direction approximation, matching similarity