Water resources are an indispensable precious resource for human survival and development. Water quality prediction plays a vital role in protecting and enhancing water resources. Changes in water quality are influenced by many factors, both long-term and short-term. Therefore, according to water quality changes’ periodic and nonlinear characteristics, this paper considered dissolved oxygen as the research object and constructed a neural network model combining convolutional neural network (CNN) and long short-term memory network (LSTM) to predict dissolved oxygen index in water quality. Firstly, we preprocessed the water quality data set obtained from the water quality monitoring platform. Secondly, we used a CNN network to extract local features from the preprocessed water quality data and transferred time series with better expressive power than the original water quality information to the LSTM layer for prediction. We choose optimal parameters by setting the number of neurons in the LSTM network and the size and number of convolution kernels in the CNN network. Finally, LSTM and the proposed model were used to evaluate the water quality data. Experiments showed that the proposed model is more accurate than the conventional LSTM in the prediction effect of peak fitting. Compared with the conventional LSTM model, its root mean square error, Pearson correlation coefficient, mean absolute error and mean square error were respectively optimized by 5.99%, 2.80%, 2.24%, and 11.63%.

With the rapid development of social humanities and technology, especially the development of the Internet, the current Internet era, the amount of information is increasing rapidly with explosive growth rate, the information overload problem is particularly obvious, on the accurate acquisition of information from the massive amount of information users want, from which the personalized recommendation technology was born. In order to solve the problem of acquiring the information users want, this paper researches and discusses a kind of personalized recommendation algorithm - a user-based collaborative filtering algorithm, analyzing the user behavior, comparing the advantages and disadvantages of other related algorithms, using the UserCF algorithm, and optimizing the sparse matrix to reduce the time and complexity of the operation. The algorithm is implemented by software to generate recommendation results. The results of the experimental data in this paper show that the algorithm is effective in recommending projects to users.

Using the RFM (Recency, Frequency, Monetary value) model can provide valuable insights about customer clusters which is the core of customer relationship management. Due to accurate customer segment coming from dynamic weighted applications, in-depth targeted marketing may also use type of dynamic weight of R, F and M as factors. In this paper, we present our dynamic weighted RFM approach which is intended to improve the performance of customer segmentation by using the factors and variations to attain dynamic weights. Our dynamic weight approach is a kind of Custom method in essential which roots in the understanding of the data set. Firstly, Analytic Hierarchy Process is used to calculate the subjective weight, then the entropy method is applied to calculate the objective weight. Finally, we use comprehensive integration weighting method to combine the subjective and objective weight to obtain the final weight of the index to calculate the individual user value and quantify the user value difference. The experiment shows that the dynamic weight we used in RFM model is vital, affects the customer segmentation performance positively. Also, this study indicates that customer segments containing dynamic weighted RFM scores bring about stronger and more accurate association rules for the understanding of customer behavior. At last, we discuss the limitations of RFM analysis.