Time series is a data type frequently encountered in data analysis. With the current depth and breadth of the data and the improvement in computer processing capabilities, the dimensionality and the complexity of time series are getting higher and higher. Time series symbolization is to cluster and assign complex and lengthy time series in the form of symbols to achieve the purpose of reducing the dimensionality of the sequence or making the sequence easier to process. Considering the excellent performance of the K-means algorithm in data mining and processing, as well as in the allocation algorithm for clustering, we plan to develop a simple method for the symbolization of time series for the K-means algorithm and hope that this method can realize the high-dimensional time series dimensionality reduction, processing of the special points in time series, and so on. Based on this, this article proposes an improved sans algorithm based on the K-means algorithm and discusses the representation method and the data processing of time series symbolization. Experimental results show that this method can effectively reduce the dimensionality of high-dimensional time series. After dimensionality reduction, the information retention rate contained in the elevation of the sequence can reach more than 90%, which is very effective for the detection of outliers in low-dimensional sequences.

Computer is one of the indispensable tools in the human world, and human needs for it are increasing, so the emergence and application of more advanced computers are needed. The current computers do not respond intelligently, and it is difficult to meet people’s needs for information processing in the era of big data. In order to solve these problems, this paper proposes the application of a neural network-based data classification algorithm in computers, aiming to study the practical application of the algorithm in computers. The research method of this paper is to introduce the BP neural network, select the appropriate method of classification features, and then study the data classification algorithm. The function of the research method is to compare the classification error and convergence speed of the BP network composed of different hidden layer nodes, to study whether a certain feature item of the data exists and the difference in the amount of information classification of the entire document, and to select high efficiency, accuracy, and scalability algorithm. This paper compares the forward reasoning time of the model before and after cutting through experiments based on neural network model design, algorithm design, and man-machine dialogue model design. The results show that, in terms of computing speed, the adaptive model compression method based on the accuracy and redundancy ratio compresses the model after the forward reasoning time is greatly reduced, and the reasoning time becomes 35% of the original, and in terms of calculation accuracy, the absolute error after using the SOM method in this article has not reached 0.5.