The feasibility analysis of reconstructing environmental facilities in existing buildings based on convolutional neural network

The ability of deep convolutional neural networks (CNN) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification. With the rapid growth of China’s economy, the improvement of people’s living standards and the increasing trend of aging, the population problem has become increasingly prominent. The total number of old-age facilities in the market of our country is very scarce that showing the trend of “more monks and fewer porridges”. At the same time, the old factory buildings and office buildings in some cities are facing idleness due to the adjustment of the national economic structure. More and more existing buildings have been rebuilt into old-age facilities in the market under the influence of these two factors, it achieves a win-win effect through the replacement of functions. In this paper, a feasibility analysis model of old-age facilities reconstruction in existing buildings is established based on convolution neural network algorithm, it has guiding significance for guiding the practical application of existing building renovation and old-age facilities.