Research on the Application of Fusion Recommendation Algorithm in Optimal Design of Indoor Space Layout

The era of big data intelligence is coming, and the intelligent recommendation system provides important technical support for the design of interior space layout. In this regard, in order to solve the problem of interior space layout design, an intelligent recommendation system is introduced for optimization. First, the convolution model is used to extract the indoor spatial feature information, and the principal component method is used to reduce the dimensionality of the feature data set. In order to improve the efficiency of spatial layout, a bidirectional long-short-time network is used to build a segmentation and layout model to complete the pre-segmentation of the length of the apartment type. At the same time, a word embedding algorithm is introduced to extract the cross-features of each vector, thereby improving the accuracy of the spatial layout. The researchers found through experiments that when the number of household items is 30, the accuracy of the model is the highest at 0.986. In addition, the researchers also compared the loss performance of the three algorithms and found that the proposed fusion algorithm performed better in terms of data processing. To sum up, the proposed intelligent recommendation system can play an important role in the interior space layout design and provide important data reference for modern interior space layout and design.