Data Model of Key Indicators of Urban Architectural Design Based on Long- and Short-Term Convolutional Memory Network

Abstract

The analysis and design of key indicators of urban architecture has always been a frontier subject in the field of urban architecture. Building key indicator network area flow is challenging due to its high degree of nonlinearity and randomness. Based on the extraction method of key indicators of urban architectural design based on long and short-term convolutional memory network, this paper designs a data model of key indicators of urban architectural design. The model can effectively simulate the urban architectural design system to obtain the key index information of urban architectural design of objects step by step. The architecture of cascaded full convolutional long and short-term convolutional memory network is designed and improved. The experiment adopts the block method to ensure the output result with the same resolution as the input urban building image, which solves the problems of the traditional fully convolutional network such as small local receptive field, low output resolution. The simulation results show that the data prediction accuracy of the key indicators proposed in this paper reaches 92.3%, which is 16% and 11.5% higher than the prediction accuracy of the other two depth algorithms (76.3% and 80.8%) respectively, which promotes the key indicators of information flow within the network.