## 1 Introduction  
Shotgun proteomics based on liquid chromatography-tandem mass spectrometry has become a mainstream method for the comprehensive analysis of proteins [1, 2]. Proteomics typically involves preparing a protein sample, turning the protein into a peptide mixture via enzymology, producing a series of mass spectra, and then using tandem mass spectrometry to identify the peptide sequence. Lastly, the sample protein sequence can be put together. Protein identification is the most important step in the whole process. Bottom-up proteomics mainly uses three methods: data-dependent acquisition (DDA), to discover proteomics and achieve unbiased and complete coverage of the proteome; data-independent acquisition (DIA), which elutes all peptides from the high-performance liquid chromatography column for multiple fragmentations to generate a complete MS/MS spectrum of fragment ions for the sample; targeted proteomics (targeted), which aims to obtain a subset of known polypeptides of interest in a repeatable, sensitive and smooth manner [3]. No matter which method is used, the identification of peptides and proteins cannot be separated from the generation of mass spectrometry data. Most existing methods of protein identification are based on DDA mass spectra data [4]. In the process of DDA fragmentation, the precursor ions with the highest intensity are usually selected for fragmentation to generate MS/MS spectra. Thus, mass spectrometers are not able to reliably isolate and acquire high-quality MS/MS spectra for all peptides in typical samples [5]. As a result, inaccurate sampling and poor repeatability of analysis will occur; thus, this method is not suitable for the analysis of complex samples. In contrast to data-dependent acquisition, DIA methods [6, 7], alternative workflows to DDA, have recently made great advances; they select all precursor ions within a certain retention time and \(m/z\) range to generate MS/MS spectra. Every precursor ion in the MS1 spectrum is selected in the DIA method, and those with a lower intensity will also be recorded [8]. Usually, the instrument circulates through the \(m/z\) range of precursor ions in the specified width, generating highly multiplexed fragment mass spectra in each cycle. In addition, fragment ions in DIA mass spectrometry can be reconstructed to form MS/MS chromatograms, which can provide support for peptide identification and quantification. While DIA ensures that each precursor ion within a predefined mass range is fragmented in one cycle, the complexity of the spectrum presents a significant challenge for subsequent analysis [9]. MS/MS spectra of DIA data are difficult to interpret because they are highly complex. Each mass spectrum contains fragment ions from more than one precursor; a remaining challenge is correctly and efficiently interpreting the connection between the precursor ion and its fragment ions. The identification of pseudo-MS/MS spectra is the primary method for data-independent acquisition mass spectrometry. Various tools have been proposed, such as DeMux [10], DIA-Umpire [5], Group-DIA [11], Specter [12], and CorrDec [13]. After the peptide identification results are obtained, the estimated false discovery rate (FDR) is used to evaluate the reliability of the identification results [14]. Here, we present a new method termed CorrDIA, which utilizes a cosine-similarity-based deconvolution approach to divide a DIA mass spectrum into multiple pseudo-MS/MS spectra, each representing a single peptide ion. The method has two main contributions:  
* To address the issue of a large amount of data in chromatogram similarity comparison, which create a larger search space and require more calculations. In contrast to the prior method, the MS/MS spectra are at the center of our algorithm, and the information \"isolation window\" is added when extracting mass spectra data to reduce the comparison space, improving the accuracy of the experiment. * According to the characteristics of DIA data, there are a large number of isotopic peak clusters in the spectrum.