With the development of Web2.0 and mobile Internet, urban residents, a new type of “sensor”, provide us with massive amounts of volunteered geographic information (VGI). Quantifying the spatial patterns of VGI plays an increasingly important role in the understanding and development of urban spatial functions. Using VGI and social media activity data, this article developed a method to automatically extract and identify urban spatial patterns and functional zones. The method is put forward based on the case of Beijing, China, and includes the following three steps: (1) Obtain multi-source urban spatial data, such as Weibo data (equivalent to Twitter in Chinese), OpenStreetMap, population data, etc.; (2) Use the hierarchical clustering algorithm, term frequency-inverse document frequency (TF-IDF) method, and improved k-means clustering algorithms to identify functional zones; (3) Compare the identified results with the actual urban land uses and verify its accuracy. The experiment results proved that our method can effectively identify urban functional zones, and the results provide new ideas for the study of urban spatial patterns and have great significance in optimizing urban spatial planning.

With the development of the internet, the user behavior and emotional characteristics behind social networks have attracted scholars' attention. Meanwhile, identifying user emotion can promote the development of mobile communication technology and network intelligence industrialization. Based on this, this work explores the emotions of social network users and discusses the public comments on the speeches through the speeches of social network users. After 100 times of training, F1 of the BiLSTM algorithm can reach 97.32%, and after 100 times of training, its function loss can be reduced to 1.33%, which can reduce the impact of function loss on emotion recognition. The exploration is of great significance for analyzing the emotional behavior of social network users and provides a reference for the intelligent and systematic development of internet social model as well as the information management.

The purpose is to make for the traditional Network Public Opinion (NPO) analysis methods’ inadequacy in the era of big data and provide a sufficient decision-making basis for managers. Based on the Internet of Things (IoT) and big data, this work applies Natural Language Processing (NLP) to NPO analysis. Additionally, it takes the content of Microblog text format as the main collection target, constructs a big data collection tool, and establishes Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Deep Pyramid Convolutional Neural Network (DPCNN) based on Tensorflow and other deep learning models. It is also improved in combination with the characteristics of the model, and a new model is proposed. Finally, the performance of various models is compared and analyzed through experiments, and the path is proposed for the government to use big data to improve the ability to govern NPO and help social governance. The results show that the improved LSTM model can correctly classify the extracted Microblog text’s emotion by as much as 80.00%. It improves the classification accuracy by nearly two percentage points under the ideal condition. Thus, by adding residual connection and attention mechanism, the model can extract the emotional features in the text better and improve the emotional discrimination ability. The public opinion of online media without effective control will have great security risks to social governance under the big data and IoT. The proposed method is of great help in analyzing NPO through the accurate analysis of Microblog text.

Analysis on Time-Series Data from Movie Using MF-DCCA Method and Recurrent Neural Network Model Under the Internet of Things: The present work aims to analyze the time-series data (TSD) from movies and support constructing the movie recommendation system. Referencing the Internet of Things (IoT) technology as the framework, a time-series data analysis system for movies is built based on the recurrent neural network (RNN) and multifractal detrended mobility cross-correlation analysis (MF-DCCA) method. First, the traditional RNN model is improved by replacing the conventional convolution operation with spatial adaptive convolution. Specifically, an additional convolution layer is used to obtain the position parameters required for adaptive convolution to improve the model performance to capture the characteristics of spatial-temporal transformation. Then, the MF-DCCA method is optimized to reduce the interference of noise signals to the analysis processing of TSD from movies. Finally, the TSD analysis system is tested for performance verification. The test results indicate that the method proposed here has outstanding stability and runs smoothly. When the prediction scheme is long short-term memory (LSTM) (L=20), the similarity of the LSTM (L=20) network under one frame is 0.977; the similarity of the LSTM (L=20) network under nine frames is 0.727. This system provides a specific idea for applying the RNN model and MF-DCCA method in analyzing TSD from movies.

Este estudo examina a legitimidade histórica, o potencial estético e as limitações da imagem da tela vertical através de duas dimensões: a comparação cross media da Nova Onda do cinema francês e a prática criativa da imagem da tela vertical no complexo ambiente das novas mídias da atualidade. Fundamentado no princípio da integração entre história e lógica, este estudo explora detalhadamente a influência e a inspiração da estética narrativa dos filmes da Nouvelle Vague francesa na criação de vídeos curtos na era da tela vertical. Isso é abordado a partir de quatro dimensões: “contar” e “mostrar””, bem como tempo narrativo e espaço narrativo. Através da arqueologia midiática da história do cinema, é fácil descobrir que muitas características da criação de vídeos curtos coincidem com o conceito criativo defendido pela Nouvelle Vague francesa.