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Looking at the traditional interior space design industry, the traditional design method is mainly manual design and the use of interactive modeling software and its design process mainly relies on trial and error. This paper takes the interior space design software platform as the background to study the collocation recommendation algorithm of the 3D home model, aim at improve the efficiency of the intelligent design algorithm. The recommendation idea of collaborative filtering is simple to implement, does not need to consider the inherent attribute characteristics of three-dimensional home projects, and is fast to calculate. After constructing the image feature database, this article uses the similarity between images to measure the visual similarity of the indoor space model; uses similar home projects to predict the collocation data of adjacent projects, and densifies the sparse collocation data; constructs each image separately Feature database, and use this to build its similarity table. According to the similarity table corresponding to each item, the first simNum items of the same category that are similar to the current item can be found. The experimental results show that compared with the traditional algorithm, the algorithm in this paper has greatly improved the accuracy of collocation recommendation.
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